bluuebunny/crossref_metadata_2025_split huggingface dataset:
LMP1 enhances aerobic glycolysis in natural killer/T cell lymphoma
Authors: Song Wenting , Gao Yuyang , Wu Jiazhuo , Li Hongwen , Shi Zhuangzhuang , Gong Chen , Zhang Zihe , Li Zhaoming , Zhang Mingzhi 
Published: August 2024

AbstractNatural killer/T cell lymphoma (NKTCL) exhibits highly aggressive clinical behavior, and the outcomes for relapsed/refractory patients are still poor. Recently, the mechanism underlying the effect of Epstein-Barr virus (EBV) infection, which has not been fully defined in NKTCL, has attracted great attention. We explored how LMP1 promoted aerobic glycolysis via metabolic sequencing combined with mRNA sequencing and immunoprecipitation coupled to mass spectrometry. Experimental assays were used to determine the effects of LMP1 and its downstream pathway on the function and glucose metabolism of NKTCL cells. The correlations between LMP1 expression in patients and their clinical features, treatment response, and prognosis were analyzed. Results show that LMP1 enhances NKTCL cell proliferation in vitro and in vivo, inhibits apoptosis, and decreases gemcitabine sensitivity. In addition, LMP1 also enhances aerobic glycolysis in NKTCL cells, as indicated by increases in glucose uptake, lactate production, and extracellular acidification rate. Clinically, LMP1 expression is correlated with risk stratification, treatment response, and prognosis, and higher LMP1 expression indicates greater SUVmax for NKTCL patients. Mechanistically, LMP1 competitively binds to TRAF3 to promote cell proliferation and aerobic glycolysis by regulating the noncanonical NF-κB pathway. The application of an NF-κB pathway inhibitor or reactivation of the NF-κB pathway affects aerobic glycolysis and the biological function of NKTCL cells. In summary, this study is the first to describe and define in detail how LMP1 affects glucose metabolism in NKTCL and might provide a novel perspective for further treatment.
Linear polyubiquitylation of Gli protein regulates its protein stability and facilitates tumor growth in colorectal cancer
Authors: Cheng Junyao , Xu Linlin , Xuan Yanlu , Zhou Feifei , Huang Aidi , Zeng Shaopeng , Wang Hailong , Wang Yiting , Zhan Yuan , Yan Xiaohua , Luo Shiwen , Liu Yuan , Cheng Minzhang 
Published: August 2024

AbstractThe linear ubiquitin chain assembly complex (LUBAC) mediates the linear ubiquitination of various proteins and is involved in NF-κB signaling and immune regulation. However, the function and mechanism of linear ubiquitination in regulating oncogenic signaling and tumor growth have remained poorly understood. Herein, we identified Gli proteins, key transcription factors in the Hedgehog (Hh) signaling pathway, as novel substrates of LUBAC. Linear ubiquitination stabilizes Gli proteins, leading to the noncanonical activation of Hh signaling in CRC cells. Furthermore, LUBAC facilitates tumor growth in CRC cells. Additionally, elevated expression of LUBAC components in CRC tissues was observed, and higher expression levels of these components correlated with poor prognosis in CRC patients. Interestingly, inhibition of LUBAC using either a small molecule agonist or RNA silencing specifically suppressed cell growth in CRC cells but had no effect on normal intestinal cells. Taken together, aberrant expression of LUBAC components activates Hh signaling noncanonically by mediating linear ubiquitination, promoting tumor growth in CRC, demonstrating the novel function of linear ubiquitination in regulating the protein stability of its substrates and highlighting the potential of targeting LUBAC as a therapeutic strategy in CRC.
Stem cell transcriptional profiles from mouse subspecies reveal cis-regulatory evolution at translation genes
Authors: Simon Noah M. , Kim Yujin , Gribnau Joost , Bautista Diana M. , Dutton James R. , Brem Rachel B. 
Published: August 2024

AbstractA key goal of evolutionary genomics is to harness molecular data to draw inferences about selective forces that have acted on genomes. The field progresses in large part through the development of advanced molecular-evolution analysis methods. Here we explored the intersection between classical sequence-based tests for selection and an empirical expression-based approach, using stem cells from Mus musculus subspecies as a model. Using a test of directional, cis-regulatory evolution across genes in pathways, we discovered a unique program of induction of translation genes in stem cells of the Southeast Asian mouse M. m. castaneus relative to its sister taxa. We then mined population-genomic sequences to pursue underlying regulatory mechanisms for this expression divergence, finding robust evidence for alleles unique to M. m. castaneus at the upstream regions of the translation genes. We interpret our data under a model of changes in lineage-specific pressures across Mus musculus in stem cells with high translational capacity. Our findings underscore the rigor of integrating expression and sequence-based methods to generate hypotheses about evolutionary events from long ago.
Fish-hunting cone snail disrupts prey’s glucose homeostasis with weaponized mimetics of somatostatin and insulin
Authors: Yeung Ho Yan , Ramiro Iris Bea L. , Andersen Daniel B. , Koch Thomas Lund , Hamilton Alexander , Bjørn-Yoshimoto Walden E. , Espino Samuel , Vakhrushev Sergey Y. , Pedersen Kasper B. , de Haan Noortje , Hipgrave Ederveen Agnes L. , Olivera Baldomero M. , Knudsen Jakob G. , Bräuner-Osborne Hans , Schjoldager Katrine T. , Holst Jens Juul , Safavi-Hemami Helena 
Published: August 2024

AbstractVenomous animals have evolved diverse molecular mechanisms to incapacitate prey and defend against predators. Most venom components disrupt nervous, locomotor, and cardiovascular systems or cause tissue damage. The discovery that certain fish-hunting cone snails use weaponized insulins to induce hypoglycemic shock in prey highlights a unique example of toxins targeting glucose homeostasis. Here, we show that, in addition to insulins, the deadly fish hunter, Conus geographus, uses a selective somatostatin receptor 2 (SSTR2) agonist that blocks the release of the insulin-counteracting hormone glucagon, thereby exacerbating insulin-induced hypoglycemia in prey. The native toxin, Consomatin nG1, exists in several proteoforms with a minimized vertebrate somatostatin-like core motif connected to a heavily glycosylated N-terminal region. We demonstrate that the toxin’s N-terminal tail closely mimics a glycosylated somatostatin from fish pancreas and is crucial for activating the fish SSTR2. Collectively, these findings provide a stunning example of chemical mimicry, highlight the combinatorial nature of venom components, and establish glucose homeostasis as an effective target for prey capture.
Rapid single-tier serodiagnosis of Lyme disease
Authors: Ghosh Rajesh , Joung Hyou-Arm , Goncharov Artem , Palanisamy Barath , Ngo Kevin , Pejcinovic Katarina , Krockenberger Nicole , Horn Elizabeth J. , Garner Omai B. , Ghazal Ezdehar , O’Kula Andrew , Arnaboldi Paul M. , Dattwyler Raymond J. , Ozcan Aydogan , Di Carlo Dino 
Published: August 2024

AbstractPoint-of-care serological and direct antigen testing offers actionable insights for diagnosing challenging illnesses, empowering distributed health systems. Here, we report a POC-compatible serologic test for Lyme disease (LD), leveraging synthetic peptides specific to LD antibodies and a paper-based platform for rapid, and cost-effective diagnosis. Antigenic epitopes conserved across Borrelia burgdorferi genospecies, targeted by IgG and IgM antibodies, are selected to develop a multiplexed panel for detection of LD antibodies from patient sera. Multiple peptide epitopes, when combined synergistically with a machine learning-based diagnostic model achieve high sensitivity without sacrificing specificity. Blinded validation with 15 LD-positive and 15 negative samples shows 95.5% sensitivity and 100% specificity. Blind testing with the CDC’s LD repository samples confirms the test accuracy, matching lab-based two-tier results, correctly differentiating between LD and look-alike diseases. This LD diagnostic test could potentially replace the cumbersome two-tier testing, improving diagnosis and enabling earlier treatment while facilitating immune monitoring and surveillance.
Drivers and Impacts of the Record-Breaking 2023 Wildfire Season in Canada
Authors: Jain Piyush , Barber Quinn E. , Taylor Stephen W. , Whitman Ellen , Castellanos Acuna Dante , Boulanger Yan , Chavardès Raphaël D. , Chen Jack , Englefield Peter , Flannigan Mike , Girardin Martin P. , Hanes Chelene C. , Little John , Morrison Kimberly , Skakun Rob S. , Thompson Dan K. , Wang Xianli , Parisien Marc-André 
Published: August 2024

AbstractThe 2023 wildfire season in Canada was unprecedented in its scale and intensity, spanning from mid-April to late October and across much of the forested regions of Canada. Here, we summarize the main causes and impacts of this exceptional season. The record-breaking total area burned (~15 Mha) can be attributed to several environmental factors that converged early in the season: early snowmelt, multiannual drought conditions in western Canada, and the rapid transition to drought in eastern Canada. Anthropogenic climate change enabled sustained extreme fire weather conditions, as the mean May–October temperature over Canada in 2023 was 2.2 °C warmer than the 1991–2020 average. The impacts were profound with more than 200 communities evacuated, millions exposed to hazardous air quality from smoke, and unmatched demands on fire-fighting resources. The 2023 wildfire season in Canada not only set new records, but highlights the increasing challenges posed by wildfires in Canada.
Assessing the clinical utility of inertial sensors for home monitoring in Parkinson’s disease: a comprehensive review
Authors: Sapienza Stefano , Tsurkalenko Olena , Giraitis Marijus , Mejia Alan Castro , Zelimkhanov Gelani , Schwaninger Isabel , Klucken Jochen 
Published: August 2024

AbstractThis review screened 296 articles on wearable sensors for home monitoring of people with Parkinson’s Disease within the PubMed Database, from January 2017 to May 2023. A three-level maturity framework was applied for classifying the aims of 59 studies included: demonstrating technical efficacy, diagnostic sensitivity, or clinical utility. As secondary analysis, user experience (usability and patient adherence) was evaluated. The evidences provided by the studies were categorized and stratified according to the level of maturity. Our results indicate that approximately 75% of articles investigated diagnostic sensitivity, i.e. correlation of sensor-data with clinical parameters. Evidence of clinical utility, defined as improvement on health outcomes or clinical decisions after the use of the wearables, was found only in nine papers. A third of the articles included reported evidence of user experience. Future research should focus more on clinical utility, to facilitate the translation of research results within the management of Parkinson’s Disease.
Uncovering patterns of inhaler technique and reliever use: the value of objective, personalized data from a digital inhaler
Authors: Levy Mark L. , Kocks Janwillem W. H. , Bosnic-Anticevich Sinthia , Safioti Guilherme , Reich Michael , Depietro Michael , Castro Mario , Farooqui Nabeel , Lugogo Njira L. , Brown Randall , Hill Tanisha , Li Thomas , Chrystyn Henry 
Published: August 2024

AbstractElectronic inhalers provide information about patterns of routine inhaler use. During a 12-week study, 360 asthma patients using albuterol Digihaler generated 53,083 inhaler events that were retrospectively analyzed. A total of 41,528 (78%) of the recorded inhalation events were suitable for flow analysis (having a PIF ≥ 18 L/min and <120 L/min). Median PIF, inhalation volume, inhalation duration, and time to PIF for these events steadily decreased between the first and last 10 days of the study, by 5.1%, 12.6%, 15.9%, and 6.4%, respectively. Continuous short-acting beta2-agonist (SABA) overuse, defined as ≥2 SABA inhalations/week throughout the study period, was seen in 29% (n = 104) of patients. Of 260 patients with ≥1 instance of acute short-term SABA overuse, 55 (21%) had a confirmed exacerbation. Electronic recording of real-life inhaler use can capture valuable, objective information that could inform disease management and clinical decision-making.
Identifying sarcoidosis trends using web search and real-world data in Sweden: a retrospective longitudinal study
Authors: Ziehfreund Stefanie , Tizek Linda , Arkema Elizabeth V. , Zink Alexander 
Published: August 2024

AbstractWeb search data are associated with disease incidence, population interest, and seasonal variations. This study aimed to investigate seasonal and geographical variations of web search data for sarcoidosis and to explore its association with external factors and sarcoidosis incidence in Sweden. Therefore, sarcoidosis-related data from Google Ads Keyword Planer (2017–2020) were generated for Sweden according to its 21 counties. The relationship between search volume and season, region, population demographics, environmental factors, and the sarcoidosis incidence listed in the National Patient Register was assessed. Analyses revealed seasonal variations for Sweden with an overall peak in the spring and autumn. Geographical differences were observed, with a higher search volume for north-western counties and the lowest search volume for Stockholm County. At the country level, the search volume was positively associated with the sarcoidosis incidence. Higher male proportion and older mean age were associated with a higher search volume, while a higher proportion of foreign-born residents, humidity, and mean temperature were associated with a lower search volume. Our analyses detected correlations between web search data, sarcoidosis incidence, and external factors. Analyses of sarcoidosis web search data therefore appear to be a valuable approach to disease surveillance to address medical needs and public interest.
The recovery of parabolic avalanches in spatially subsampled neuronal networks at criticality
Authors: Srinivasan Keshav , Ribeiro Tiago L. , Kells Patrick , Plenz Dietmar 
Published: August 2024

AbstractScaling relationships are key in characterizing complex systems at criticality. In the brain, they are evident in neuronal avalanches—scale-invariant cascades of neuronal activity quantified by power laws. Avalanches manifest at the cellular level as cascades of neuronal groups that fire action potentials simultaneously. Such spatiotemporal synchronization is vital to theories on brain function yet avalanche synchronization is often underestimated when only a fraction of neurons is observed. Here, we investigate biases from fractional sampling within a balanced network of excitatory and inhibitory neurons with all-to-all connectivity and critical branching process dynamics. We focus on how mean avalanche size scales with avalanche duration. For parabolic avalanches, this scaling is quadratic, quantified by the scaling exponent, χ = 2, reflecting rapid spatial expansion of simultaneous neuronal firing over short durations. However, in networks sampled fractionally, χ is significantly lower. We demonstrate that applying temporal coarse-graining and increasing a minimum threshold for coincident firing restores χ = 2, even when as few as 0.1% of neurons are sampled. This correction crucially depends on the network being critical and fails for near sub- and supercritical branching dynamics. Using cellular 2-photon imaging, our approach robustly identifies χ = 2 over a wide parameter regime in ongoing neuronal activity from frontal cortex of awake mice. In contrast, the common ‘crackling noise’ approach fails to determine χ under similar sampling conditions at criticality. Our findings overcome scaling bias from fractional sampling and demonstrate rapid, spatiotemporal synchronization of neuronal assemblies consistent with scale-invariant, parabolic avalanches at criticality.
Effect of different inner pressures of air insoles and walking durations on plantar pressure time integral
Authors: Ramadhan Gilang Titah , Haris Fahni , Jan Yih-Kuen , Liau Ben-Yi , Chang Wen-Thong , Tai Chien-Cheng , Lung Chi-Wen 
Published: August 2024

AbstractAir insoles have provided insights for reducing the risk of diabetic foot ulcers (DFU). The pressure time integral (PTI) is an effective assessment that considers the time effect in various physical activities. We investigated the interactions between three different insole inner pressures (80, 160, and 240 mmHg) and two walking durations (10 and 20 min). The big toe (T1), first metatarsal head (M1), and second metatarsal head (M2) were investigated in 13 healthy participants. One-way analysis of variance (ANOVA) showed that the effects of each insole inner pressure significantly differed (P < 0.05) with a 10 min walking duration. The PTI values resulting from 80 mmHg in M2 (38.4 ± 3.8, P = 0.002) and 160 mmHg in M1 (44.3 ± 4.3, P = 0.027) were lower than those from 240 mmHg. Additionally, the paired t test showed that the effects of each walking duration were also considerably different at 160 mmHg. The PTI at 10 min was lower than that at 20 min in M1 (44.31 ± 4.31, P = 0.015) and M2 (47.14 ± 5.27, P = 0.047). Thus, we suggest that walking with a pressure of 160 mmHg for 10 min has a lower risk of DFU.
An in situ crosslinked matrix enables efficient and mechanically robust organic solar cells with frozen nano-morphology and superior deformability
Authors: Song Wei , Chen Zhenyu , Lin Congqi , Zhang Pilan , Sun Dinghong , Zhang Weifu , Ge Jinfeng , Xie Lin , Peng Ruixiang , Yang Daobin , Liu Quan , Xu Yifei , Ge Ziyi 
Published: August 2024

An in-situ cross-linkable monomer is carefully developed into blend films to finely manipulate the molecular packing, crystallization and nanomorphology during film formation. As a result, stabilized PCEs of 19.84% for the rigid device and 18.32% for flexible organic solar cells are achieved.
Promoting convergence and efficacy of open‐domain question answering via unsupervised clustering
Authors: Liu Shuoyan , Han Qiuchi 
Published: August 2024

AbstractOpen‐Domain Question Answering (ODQA) has attracted increasing interests due to its extensive applications in search engines and smart robots. In the experiments, it is observed that the convergence of the method has a huge effect on the generalizability performance. Motivated by this observation, an unsupervised clustering technique (namely, ClusSampling) is proposed to promote both the convergence and efficacy of existing ODQA methods via unsupervised clustering. Specifically, unsupervised clustering is first conducted and then negative samples are selected for higher similarity to the questions. In addition, the authors propose to use gap statistics to determine the optimal number of clusters. Experimental results show that the method achieves notable speedup during training and produces accuracy gains of 5.3% and 2.2 on two widely used benchmarks.
Glaucoma detection with explainable AI using convolutional neural networks based feature extraction and machine learning classifiers
Authors: Velpula Vijaya Kumar , Sharma Diksha , Sharma Lakhan Dev , Roy Amarjit , Bhuyan Manas Kamal , Alfarhood Sultan , Safran Mejdl 
Published: August 2024

AbstractGlaucoma is an eye disease that damages the optic nerve as a result of vision loss, it is the leading cause of blindness worldwide. Due to the time‐consuming, inaccurate, and manual nature of traditional methods, automation in glaucoma detection is important. This paper proposes an explainable artificial intelligence (XAI) based model for automatic glaucoma detection using pre‐trained convolutional neural networks (PCNNs) and machine learning classifiers (MLCs). PCNNs are used as feature extractors to obtain deep features that can capture the important visual patterns and characteristics from fundus images. Using extracted features MLCs then classify glaucoma and healthy images. An empirical selection of the CNN and MLC parameters has been made in the performance evaluation. In this work, a total of 1,865 healthy and 1,590 glaucoma images from different fundus datasets were used. The results on the ACRIMA dataset show an accuracy, precision, and recall of 98.03%, 97.61%, and 99%, respectively. Explainable artificial intelligence aims to create a model to increase the user's trust in the model's decision‐making process in a transparent and interpretable manner. An assessment of image misclassification has been carried out to facilitate future investigations.
Real‐time pricing for smart grid with multiple energy coexistence on the user side
Authors: Zhao Na , Lv Jianfeng , Gao Yan , Tang Jun , Zhou Fuying 
Published: August 2024

AbstractWith the development of new technology for multiple energy applications, household appliances using electricity and other energy sources are becoming increasingly abundant. Users can freely choose the way of energy consumption based on the price of electricity and other energy sources. The phenomenon has become one of the important factors affecting real‐time pricing. This study considers real‐time pricing in the case of multiple energy coexistence on the user side. According to the spatiotemporal coupling of electricity and other energy sources and the relationship between other energy sources and total energy consumption, an optimization model that satisfies the actual situation is established. By using the relaxation theory and dual decomposition technology, a partially distributed algorithm is proposed for multi‐period coupling and finding the optimal energy scheduling scheme for the energy company and each user, so as to maximize social welfare. In addition, under the background of multiple energy coupling, real‐time pricing can guide users to adjust their electricity consumption behaviour vertically and horizontally, change the type of energy, meet energy demand for end users and effectively reduce the peak‐to‐average ratio. Simulation results verify the rationality of the model and the effectiveness of the algorithm.
A novel prediction method for low wind output processes under very few samples based on improved W‐DCGAN
Authors: Liu Shihua , Wang Han , Song Weiye , Han Shuang , Yan Jie , Liu Yongqian 
Published: August 2024

AbstractThe threat of long‐term low wind output processes (LWOP) on the supply ability of the power system is escalating with the increasing integration of wind power. Accurate prediction of LWOP is crucial for maintaining the stable operation of the power system. However, the occurrence probability of LWOP is low and the available samples are lacking, limiting the high‐accuracy predictive modeling of LWOP. Therefore, a novel prediction method for LWOP under very few samples based on improved Wasserstein deep convolutional generative adversarial networks (W‐DCGAN) is proposed here. Firstly, a multi‐dimensional identification method is proposed to accurately identify historical LWOP. Then, an LWOP sample generation model based on improved W‐DCGAN is established. The model integrates a long short‐term memory layer into the deconvolutional layer of the generator to enhance the temporal characteristics of generated samples. Finally, three prediction algorithms are used to construct LWOP prediction models based on both generated and actual samples, respectively. The wind power operation data from a province in China is taken as an example to verify the effectiveness of the proposed method. The results show that the prediction accuracy of LWOP can be improved by 14.36%–55.85%.