bluuebunny/crossref_metadata_2025_split huggingface dataset:
Stimulus type shapes the topology of cellular functional networks in mouse visual cortex
Authors: Tang Disheng , Zylberberg Joel , Jia Xiaoxuan , Choi Hannah 
Published: July 2024

AbstractOn the timescale of sensory processing, neuronal networks have relatively fixed anatomical connectivity, while functional interactions between neurons can vary depending on the ongoing activity of the neurons within the network. We thus hypothesized that different types of stimuli could lead those networks to display stimulus-dependent functional connectivity patterns. To test this hypothesis, we analyzed single-cell resolution electrophysiological data from the Allen Institute, with simultaneous recordings of stimulus-evoked activity from neurons across 6 different regions of mouse visual cortex. Comparing the functional connectivity patterns during different stimulus types, we made several nontrivial observations: (1) while the frequencies of different functional motifs were preserved across stimuli, the identities of the neurons within those motifs changed; (2) the degree to which functional modules are contained within a single brain region increases with stimulus complexity. Altogether, our work reveals unexpected stimulus-dependence to the way groups of neurons interact to process incoming sensory information.
Estimation of joint torque in dynamic activities using wearable A-mode ultrasound
Authors: Jin Yichu , Alvarez Jonathan T. , Suitor Elizabeth L. , Swaminathan Krithika , Chin Andrew , Civici Umut S. , Nuckols Richard W. , Howe Robert D. , Walsh Conor J. 
Published: July 2024

AbstractThe human body constantly experiences mechanical loading. However, quantifying internal loads within the musculoskeletal system remains challenging, especially during unconstrained dynamic activities. Conventional measures are constrained to laboratory settings, and existing wearable approaches lack muscle specificity or validation during dynamic movement. Here, we present a strategy for estimating corresponding joint torque from muscles with different architectures during various dynamic activities using wearable A-mode ultrasound. We first introduce a method to track changes in muscle thickness using single-element ultrasonic transducers. We then estimate elbow and knee torque with errors less than 7.6% and coefficients of determination (R2) greater than 0.92 during controlled isokinetic contractions. Finally, we demonstrate wearable joint torque estimation during dynamic real-world tasks, including weightlifting, cycling, and both treadmill and outdoor locomotion. The capability to assess joint torque during unconstrained real-world activities can provide new insights into muscle function and movement biomechanics, with potential applications in injury prevention and rehabilitation.
Self-powered triboelectric-responsive microneedles with controllable release of optogenetically engineered extracellular vesicles for intervertebral disc degeneration repair
Authors: Zhang Weifeng , Qin Xuan , Li Gaocai , Zhou Xingyu , Li Hongyang , Wu Di , Song Yu , Zhao Kangcheng , Wang Kun , Feng Xiaobo , Tan Lei , Wang Bingjin , Sun Xuhui , Wen Zhen , Yang Cao 
Published: July 2024

AbstractExcessive exercise is an etiological factor of intervertebral disc degeneration (IVDD). Engineered extracellular vesicles (EVs) exhibit excellent therapeutic potential for disease-modifying treatments. Herein, we fabricate an exercise self-powered triboelectric-responsive microneedle (MN) assay with the sustainable release of optogenetically engineered EVs for IVDD repair. Mechanically, exercise promotes cytosolic DNA sensing-mediated inflammatory activation in senescent nucleus pulposus (NP) cells (the master cell population for IVD homeostasis maintenance), which accelerates IVDD. TREX1 serves as a crucial nuclease, and disassembly of TRAM1-TREX1 complex disrupts the subcellular localization of TREX1, triggering TREX1-dependent genomic DNA damage during NP cell senescence. Optogenetically engineered EVs deliver TRAM1 protein into senescent NP cells, which effectively reconstructs the elimination function of TREX1. Triboelectric nanogenerator (TENG) harvests mechanical energy and triggers the controllable release of engineered EVs. Notably, an optogenetically engineered EV-based targeting treatment strategy is used for the treatment of IVDD, showing promising clinical potential for the treatment of degeneration-associated disorders.
Neutrophil extracellular traps promote immunopathogenesis of virus-induced COPD exacerbations
Authors: Katsoulis Orestis , Toussaint Marie , Jackson Millie M. , Mallia Patrick , Footitt Joseph , Mincham Kyle T. , Meyer Garance F. M. , Kebadze Tata , Gilmour Amy , Long Merete , Aswani Andrew D. , Snelgrove Robert J. , Johnston Sebastian L. , Chalmers James D. , Singanayagam Aran 
Published: July 2024

AbstractRespiratory viruses are a major trigger of exacerbations in chronic obstructive pulmonary disease (COPD). Airway neutrophilia is a hallmark feature of stable and exacerbated COPD but roles played by neutrophil extracellular traps (NETS) in driving disease pathogenesis are unclear. Here, using human studies of experimentally-induced and naturally-occurring exacerbations we identify that rhinovirus infection induces airway NET formation which is amplified in COPD and correlates with magnitude of inflammation and clinical exacerbation severity. We show that inhibiting NETosis protects mice from immunopathology in a model of virus-exacerbated COPD. NETs drive inflammation during exacerbations through release of double stranded DNA (dsDNA) and administration of DNAse in mice has similar protective effects. Thus, NETosis, through release of dsDNA, has a functional role in the pathogenesis of COPD exacerbations. These studies open up the potential for therapeutic targeting of NETs or dsDNA as a strategy for treating virus-exacerbated COPD.
PKM2 aggregation drives metabolism reprograming during aging process
Authors: Bie Juntao , Li Ridong , Li Yutong , Song Chen , Chen Zhaoming , Zhang Tianzhuo , Tang Zhiheng , Su Li , Zhu Liangyi , Wang Jiaxin , Wan You , Chen Jun , Liu Xiaoyun , Li Tingting , Luo Jianyuan 
Published: July 2024

AbstractWhile protein aggregation’s association with aging and age-related diseases is well-established, the specific proteins involved and whether dissolving them could alleviate aging remain unclear. Our research addresses this gap by uncovering the role of PKM2 aggregates in aging. We find that PKM2 forms aggregates in senescent cells and organs from aged mice, impairing its enzymatic activity and glycolytic flux, thereby driving cells into senescence. Through a rigorous two-step small molecule library screening, we identify two compounds, K35 and its analog K27, capable of dissolving PKM2 aggregates and alleviating senescence. Further experiments show that treatment with K35 and K27 not only alleviate aging-associated signatures but also extend the lifespan of naturally and prematurely aged mice. These findings provide compelling evidence for the involvement of PKM2 aggregates in inducing cellular senescence and aging phenotypes, and suggest that targeting these aggregates could be a promising strategy for anti-aging drug discovery.
EMBER creates a unified space for independent breast cancer transcriptomic datasets enabling precision oncology
Authors: Ronchi Carlos , Haider Syed , Brisken Cathrin 
Published: July 2024

AbstractTranscriptomics has revolutionized biomedical research and refined breast cancer subtyping and diagnostics. However, wider use in clinical practice is hampered for a number of reasons including the application of transcriptomic signatures as single sample predictors. Here, we present an embedding approach called EMBER that creates a unified space of 11,000 breast cancer transcriptomes and predicts phenotypes of transcriptomic profiles on a single sample basis. EMBER accurately captures the five molecular subtypes. Key biological pathways, such as estrogen receptor signaling, cell proliferation, DNA repair, and epithelial-mesenchymal transition determine sample position in the space. We validate EMBER in four independent patient cohorts and show with samples from the window trial, POETIC, that it captures clinical responses to endocrine therapy and identifies increased androgen receptor signaling and decreased TGFβ signaling as potential mechanisms underlying intrinsic therapy resistance. Of direct clinical importance, we show that the EMBER-based estrogen receptor (ER) signaling score is superior to the immunohistochemistry (IHC) based ER index used in current clinical practice to select patients for endocrine therapy. As such, EMBER provides a calibration and reference tool that paves the way for using RNA-seq as a standard diagnostic and predictive tool for ER+ breast cancer.
The miniature genome of broad mite, Polyphagotarsonemus latus (Tarsonemidae: Acari)
Authors: Mohan Muthugounder , Augustine Neenu , Selvamani Selva Babu , P. J. Aneesha , Selvapandian Upasna , Pathak Jyoti , Gracy R. Gandhi , Thiruvengadam Venkatesan , S. N. Sushil 
Published: July 2024

AbstractThe broad mite, Polyphagotarsonemus latus (Tarsonemidae: Acari) is a highly polyphagous species that damage plant species spread across 57 different families. This pest has developed high levels of resistance to some commonly used acaricides. In the present investigation, we deciphered the genome information of P. latus by PacBio HiFi sequencing. P. latus is the third smallest arthropod genome sequenced so far with a size of 49.1 Mb. The entire genome was assembled into two contigs. A set of 9,286 protein-coding genes were annotated. Its compact genome size could be credited with multiple features such as very low repeat content (5.1%) due to the lack of proliferation of transposable elements, high gene density (189.1/Mb), more intronless genes (20.3%) and low microsatellite density (0.63%).
Integration of ten years of daily weather, traffic, and air pollution data from Norway’s six largest cities
Authors: Cao Cong 
Published: July 2024

AbstractThis study integrates ten years of daily weather, traffic, and air pollution data across the six largest Norwegian cities, utilizing data from the Norwegian Public Roads Administration, the Norwegian Institute of Air Research, and the Norwegian Meteorological Institute. The compilation of this dataset involved detailed selection and verification of monitoring stations to ensure consistency and accuracy. Initial data collection focused on the top ten most populous cities in Norway, with the subsequent examination of traffic and air pollution monitoring sites. Weather variables were then matched to the selected sites, resulting in a comprehensive dataset from 2009 to 2018. The resulting dataset encompasses extensive information, including harmful pollutants such as Nitric oxide (NO), Nitrogen dioxide (NO2), Nitrogen oxides (NOx), Particulate Matter less than 2.5 micrometers in diameter (PM2.5), and Particulate Matter less than 10 micrometers in diameter (PM10). The dataset’s potential for further analysis and its utility in informing policy decisions underscore its significance. This integrated dataset is a valuable resource for researchers and policymakers alike, facilitating comprehensive studies on the intersection of weather, traffic, and air pollution in urban environments.
Using artificial agents to nudge outgroup altruism and reduce ingroup favoritism in human-agent interaction
Authors: Igwe Kevin , Durrheim Kevin 
Published: July 2024

AbstractIngroup favoritism and intergroup discrimination can be mutually reinforcing during social interaction, threatening intergroup cooperation and the sustainability of societies. In two studies (N = 880), we investigated whether promoting prosocial outgroup altruism would weaken the ingroup favoritism cycle of influence. Using novel methods of human-agent interaction via a computer-mediated experimental platform, we introduced outgroup altruism by (i) nonadaptive artificial agents with preprogrammed outgroup altruistic behavior (Study 1; N = 400) and (ii) adaptive artificial agents whose altruistic behavior was informed by the prediction of a machine learning algorithm (Study 2; N = 480). A rating task ensured that the observed behavior did not result from the participant’s awareness of the artificial agents. In Study 1, nonadaptive agents prompted ingroup members to withhold cooperation from ingroup agents and reinforced ingroup favoritism among humans. In Study 2, adaptive agents were able to weaken ingroup favoritism over time by maintaining a good reputation with both the ingroup and outgroup members, who perceived agents as being fairer than humans and rated agents as more human than humans. We conclude that a good reputation of the individual exhibiting outgroup altruism is necessary to weaken ingroup favoritism and improve intergroup cooperation. Thus, reputation is important for designing nudge agents.
Factors of acute respiratory infection among under-five children across sub-Saharan African countries using machine learning approaches
Authors: Fenta Haile Mekonnen , Zewotir Temesgen T. , Naidoo Saloshni , Naidoo Rajen N. , Mwambi Henry 
Published: July 2024

AbstractSymptoms of Acute Respiratory infections (ARIs) among under-five children are a global health challenge. We aimed to train and evaluate ten machine learning (ML) classification approaches in predicting symptoms of ARIs reported by mothers among children younger than 5 years in sub-Saharan African (sSA) countries. We used the most recent (2012–2022) nationally representative Demographic and Health Surveys data of 33 sSA countries. The air pollution covariates such as global annual surface particulate matter (PM 2.5) and the nitrogen dioxide available in the form of raster images were obtained from the National Aeronautics and Space Administration (NASA). The MLA was used for predicting the symptoms of ARIs among under-five children. We randomly split the dataset into two, 80% was used to train the model, and the remaining 20% was used to test the trained model. Model performance was evaluated using sensitivity, specificity, accuracy, and the area under the receiver operating characteristic curve. A total of 327,507 under-five children were included in the study. About 7.10, 4.19, 20.61, and 21.02% of children reported symptoms of ARI, Severe ARI, cough, and fever in the 2 weeks preceding the survey years respectively. The prevalence of ARI was highest in Mozambique (15.3%), Uganda (15.05%), Togo (14.27%), and Namibia (13.65%,), whereas Uganda (40.10%), Burundi (38.18%), Zimbabwe (36.95%), and Namibia (31.2%) had the highest prevalence of cough. The results of the random forest plot revealed that spatial locations (longitude, latitude), particulate matter, land surface temperature, nitrogen dioxide, and the number of cattle in the houses are the most important features in predicting the diagnosis of symptoms of ARIs among under-five children in sSA. The RF algorithm was selected as the best ML model (AUC = 0.77, Accuracy = 0.72) to predict the symptoms of ARIs among children under five. The MLA performed well in predicting the symptoms of ARIs and associated predictors among under-five children across the sSA countries. Random forest MLA was identified as the best classifier to be employed for the prediction of the symptoms of ARI among under-five children.
Noninvasive, label-free image approaches to predict multimodal molecular markers in pluripotency assessment
Authors: Akiyoshi Ryutaro , Hase Takeshi , Sathiyananthavel Mayuri , Ghosh Samik , Kitano Hiroaki , Yachie Ayako 
Published: July 2024

AbstractManufacturing regenerative medicine requires continuous monitoring of pluripotent cell culture and quality assessment while eliminating cell destruction and contaminants. In this study, we employed a novel method to monitor the pluripotency of stem cells through image analysis, avoiding the traditionally used invasive procedures. This approach employs machine learning algorithms to analyze stem cell images to predict the expression of pluripotency markers, such as OCT4 and NANOG, without physically interacting with or harming cells. We cultured induced pluripotent stem cells under various conditions to induce different pluripotent states and imaged the cells using bright-field microscopy. Pluripotency states of induced pluripotent stem cells were assessed using invasive methods, including qPCR, immunostaining, flow cytometry, and RNA sequencing. Unsupervised and semi-supervised learning models were applied to evaluate the results and accurately predict the pluripotency of the cells using only image analysis. Our approach directly links images to invasive assessment results, making the analysis of cell labeling and annotation of cells in images by experts dispensable. This core achievement not only contributes for safer and more reliable stem cell research but also opens new avenues for real-time monitoring and quality control in regenerative medicine manufacturing. Our research fills an important gap in the field by providing a viable, noninvasive alternative to traditional invasive methods for assessing pluripotency. This innovation is expected to make a significant contribution to improving regenerative medicine manufacturing because it will enable a more detailed and feasible understanding of cellular status during the manufacturing process.
Differential responses to thrombospondin-1 and PDGF-BB in smooth muscle cells from atherosclerotic coronary arteries and internal thoracic arteries
Authors: Pathak Alokkumar S. , Stouffer George A. 
Published: July 2024

AbstractAtherosclerosis is rare in internal thoracic arteries (ITA) even in patients with severe atherosclerotic coronary artery (ACA) disease. To explore cellular differences, ITA SMC from 3 distinct donors and ACA SMC from 3 distinct donors were grown to sub-confluence and growth arrested for 48 h. Proliferation and thrombospondin-1 (TSP1) production were determined using standard techniques. ITA SMC were larger, grew more slowly and survived more passages than ACA SMC. ACA SMC had a more pronounced proliferative response to 10% serum than ITA SMC. Both ACA SMC and ITA SMC proliferated in response to exogenous TSP1 (12.5 µg/ml and 25 µg/ml) and platelet derived growth factor-BB (PDGF-BB; 20 ng/ml) but TSP1- and PDGF-BB-induced proliferation were partially inhibited by anti-TSP1 antibody A4.1, microRNA-21(miR-21)-3p inhibitors and miR-21-5p inhibitors in each of the 3 ACA SMC lines, but not in any of the ITA SMC lines. PDGF-BB stimulated TSP1 production in ACA SMC but not in ITA SMC but there was no increase in TSP1 levels in conditioned media in either SMC type. In summary, there are significant differences in morphology, proliferative capacity and in responses to TSP1 and PDGF-BB in SMC derived from ITA compared to SMC derived from ACA.
The prevalence, risk factors, and outcomes of acute pulmonary embolism complicating sepsis and septic shock: a national inpatient sample analysis
Authors: Hasegawa Daisuke , Sato Ryota , Lee Young Im , Wang Hong Yu , Nishida Kazuki , Steiger David 
Published: July 2024

AbstractThe study aimed to evaluate the prevalence, risk factors, and clinical outcomes of pulmonary embolism in patients diagnosed with sepsis with and without shock. The National Inpatient Sample was used to identify adults with sepsis with and without shock between 2017 and 2019. The prevalence of acute pulmonary embolism and the association of acute pulmonary embolism with in-hospital mortality, hospital length of stay for survivors, and overall costs of hospitalization were evaluated. Multivariable logistic and linear regression analyses, adjusted for various parameters, were used to explore these associations. Of the estimated 5,019,369 sepsis hospitalizations, 1.2% of patients with sepsis without shock and 2.3% of patients with septic shock developed pulmonary embolism. The odds ratio for in-hospital mortality was 1.94 (95% confidence interval (CI) 1.85–2.03, p < 0.001). The coefficient for hospital length of stay was 3.24 (95% CI 3.03–3.45, p < 0.001). The coefficient for total costs was 46,513 (95% CI 43,079–49,947, p < 0.001). The prevalence of pulmonary embolism in patients diagnosed with sepsis with and without shock was 1.2 and 2.3%, respectively. Acute pulmonary embolism was associated with higher in-hospital mortality, longer hospital length of stay for survivors, and higher overall costs of hospitalization.
Identification of Parkinson’s disease PACE subtypes and repurposing treatments through integrative analyses of multimodal data
Authors: Su Chang , Hou Yu , Xu Jielin , Xu Zhenxing , Zhou Manqi , Ke Alison , Li Haoyang , Xu Jie , Brendel Matthew , Maasch Jacqueline R. M. A. , Bai Zilong , Zhang Haotan , Zhu Yingying , Cincotta Molly C. , Shi Xinghua , Henchcliffe Claire , Leverenz James B. , Cummings Jeffrey , Okun Michael S. , Bian Jiang , Cheng Feixiong , Wang Fei 
Published: July 2024

AbstractParkinson’s disease (PD) is a serious neurodegenerative disorder marked by significant clinical and progression heterogeneity. This study aimed at addressing heterogeneity of PD through integrative analysis of various data modalities. We analyzed clinical progression data (≥5 years) of individuals with de novo PD using machine learning and deep learning, to characterize individuals’ phenotypic progression trajectories for PD subtyping. We discovered three pace subtypes of PD exhibiting distinct progression patterns: the Inching Pace subtype (PD-I) with mild baseline severity and mild progression speed; the Moderate Pace subtype (PD-M) with mild baseline severity but advancing at a moderate progression rate; and the Rapid Pace subtype (PD-R) with the most rapid symptom progression rate. We found cerebrospinal fluid P-tau/α-synuclein ratio and atrophy in certain brain regions as potential markers of these subtypes. Analyses of genetic and transcriptomic profiles with network-based approaches identified molecular modules associated with each subtype. For instance, the PD-R-specific module suggested STAT3, FYN, BECN1, APOA1, NEDD4, and GATA2 as potential driver genes of PD-R. It also suggested neuroinflammation, oxidative stress, metabolism, PI3K/AKT, and angiogenesis pathways as potential drivers for rapid PD progression (i.e., PD-R). Moreover, we identified repurposable drug candidates by targeting these subtype-specific molecular modules using network-based approach and cell line drug-gene signature data. We further estimated their treatment effects using two large-scale real-world patient databases; the real-world evidence we gained highlighted the potential of metformin in ameliorating PD progression. In conclusion, this work helps better understand clinical and pathophysiological complexity of PD progression and accelerate precision medicine.