bluuebunny/crossref_metadata_2025_split huggingface dataset:
NBS1 lactylation is required for efficient DNA repair and chemotherapy resistance
Authors: Chen Hengxing , Li Yun , Li Huafu , Chen Xiancong , Fu Huafeng , Mao Deli , Chen Wei , Lan Linxiang , Wang Chunming , Hu Kaishun , Li Jia , Zhu Chengming , Evans Ian , Cheung Eddie , Lu Daning , He Yulong , Behrens Axel , Yin Dong , Zhang Changhua 
Published: July 2024

AbstractThe Warburg effect is a hallmark of cancer that refers to the preference of cancer cells to metabolize glucose anaerobically rather than aerobically1,2. This results in substantial accumulation of lacate, the end product of anaerobic glycolysis, in cancer cells3. However, how cancer metabolism affects chemotherapy response and DNA repair in general remains incompletely understood. Here we report that lactate-driven lactylation of NBS1 promotes homologous recombination (HR)-mediated DNA repair. Lactylation of NBS1 at lysine 388 (K388) is essential for MRE11–RAD50–NBS1 (MRN) complex formation and the accumulation of HR repair proteins at the sites of DNA double-strand breaks. Furthermore, we identify TIP60 as the NBS1 lysine lactyltransferase and the ‘writer’ of NBS1 K388 lactylation, and HDAC3 as the NBS1 de-lactylase. High levels of NBS1 K388 lactylation predict poor patient outcome of neoadjuvant chemotherapy, and lactate reduction using either genetic depletion of lactate dehydrogenase A (LDHA) or stiripentol, a lactate dehydrogenase A inhibitor used clinically for anti-epileptic treatment, inhibited NBS1 K388 lactylation, decreased DNA repair efficacy and overcame resistance to chemotherapy. In summary, our work identifies NBS1 lactylation as a critical mechanism for genome stability that contributes to chemotherapy resistance and identifies inhibition of lactate production as a promising therapeutic cancer strategy.
Human degradation of tropical moist forests is greater than previously estimated
Authors: Bourgoin C. , Ceccherini G. , Girardello M. , Vancutsem C. , Avitabile V. , Beck P. S. A. , Beuchle R. , Blanc L. , Duveiller G. , Migliavacca M. , Vieilledent G. , Cescatti A. , Achard F. 
Published: July 2024

AbstractTropical forest degradation from selective logging, fire and edge effects is a major driver of carbon and biodiversity loss1–3, with annual rates comparable to those of deforestation4. However, its actual extent and long-term impacts remain uncertain at global tropical scale5. Here we quantify the magnitude and persistence of multiple types of degradation on forest structure by combining satellite remote sensing data on pantropical moist forest cover changes4 with estimates of canopy height and biomass from spaceborne6 light detection and ranging (LiDAR). We estimate that forest height decreases owing to selective logging and fire by 15% and 50%, respectively, with low rates of recovery even after 20 years. Agriculture and road expansion trigger a 20% to 30% reduction in canopy height and biomass at the forest edge, with persistent effects being measurable up to 1.5 km inside the forest. Edge effects encroach on 18% (approximately 206 Mha) of the remaining tropical moist forests, an area more than 200% larger than previously estimated7. Finally, degraded forests with more than 50% canopy loss are significantly more vulnerable to subsequent deforestation. Collectively, our findings call for greater efforts to prevent degradation and protect already degraded forests to meet the conservation pledges made at recent United Nations Climate Change and Biodiversity conferences.
Spike deep mutational scanning helps predict success of SARS-CoV-2 clades
Authors: Dadonaite Bernadeta , Brown Jack , McMahon Teagan E. , Farrell Ariana G. , Figgins Marlin D. , Asarnow Daniel , Stewart Cameron , Lee Jimin , Logue Jenni , Bedford Trevor , Murrell Ben , Chu Helen Y. , Veesler David , Bloom Jesse D. 
Published: July 2024

AbstractSARS-CoV-2 variants acquire mutations in the spike protein that promote immune evasion1 and affect other properties that contribute to viral fitness, such as ACE2 receptor binding and cell entry2,3. Knowledge of how mutations affect these spike phenotypes can provide insight into the current and potential future evolution of the virus. Here we use pseudovirus deep mutational scanning4 to measure how more than 9,000 mutations across the full XBB.1.5 and BA.2 spikes affect ACE2 binding, cell entry or escape from human sera. We find that mutations outside the receptor-binding domain (RBD) have meaningfully affected ACE2 binding during SARS-CoV-2 evolution. We also measure how mutations to the XBB.1.5 spike affect neutralization by serum from individuals who recently had SARS-CoV-2 infections. The strongest serum escape mutations are in the RBD at sites 357, 420, 440, 456 and 473; however, the antigenic effects of these mutations vary across individuals. We also identify strong escape mutations outside the RBD; however, many of them decrease ACE2 binding, suggesting they act by modulating RBD conformation. Notably, the growth rates of human SARS-CoV-2 clades can be explained in substantial part by the measured effects of mutations on spike phenotypes, suggesting our data could enable better prediction of viral evolution.
Observation of the scaling dimension of fractional quantum Hall anyons
Authors: Veillon A. , Piquard C. , Glidic P. , Sato Y. , Aassime A. , Cavanna A. , Jin Y. , Gennser U. , Anthore A. , Pierre F. 
Published: July 2024

AbstractUnconventional quasiparticles emerging in the fractional quantum Hall regime1,2 present the challenge of observing their exotic properties unambiguously. Although the fractional charge of quasiparticles has been demonstrated for nearly three decades3–5, the first convincing evidence of their anyonic quantum statistics has only recently been obtained6,7 and, so far, the so-called scaling dimension that determines the propagation dynamics of the quasiparticles remains elusive. In particular, although the nonlinearity of the tunnelling quasiparticle current should reveal their scaling dimension, the measurements fail to match theory, arguably because this observable is not robust to non-universal complications8–12. Here we expose the scaling dimension from the thermal noise to shot noise crossover and observe an agreement with expectations. Measurements are fitted to the predicted finite-temperature expression involving both the scaling dimension of the quasiparticles and their charge12,13, in contrast to previous charge investigations focusing on the high-bias shot-noise regime14. A systematic analysis, repeated on several constrictions and experimental conditions, consistently matches the theoretical scaling dimensions for the fractional quasiparticles emerging at filling factors ν = 1/3, 2/5 and 2/3. This establishes a central property of fractional quantum Hall anyons and demonstrates a powerful and complementary window into exotic quasiparticles.
Accelerating Formulation Design via Machine Learning: Generating a High-throughput Shampoo Formulations Dataset
Authors: Chitre Aniket , Querimit Robert C. M. , Rihm Simon D. , Karan Dogancan , Zhu Benchuan , Wang Ke , Wang Long , Hippalgaonkar Kedar , Lapkin Alexei A. 
Published: July 2024

AbstractLiquid formulations are ubiquitous yet have lengthy product development cycles owing to the complex physical interactions between ingredients making it difficult to tune formulations to customer-defined property targets. Interpolative ML models can accelerate liquid formulations design but are typically trained on limited sets of ingredients and without any structural information, which limits their out-of-training predictive capacity. To address this challenge, we selected eighteen formulation ingredients covering a diverse chemical space to prepare an open experimental dataset for training ML models for rinse-off formulations development. The resulting design space has an over 50-fold increase in dimensionality compared to our previous work. Here, we present a dataset of 812 formulations, including 294 stable samples, which cover the entire design space, with phase stability, turbidity, and high-fidelity rheology measurements generated on our semi-automated, ML-driven liquid formulations workflow. Our dataset has the unique attribute of sample-specific uncertainty measurements to train predictive surrogate models.
Coronary risk factor profiles according to different age categories in premature coronary artery disease patients who have undergone percutaneous coronary intervention
Authors: Nazli Sukma Azureen , Rosman Azhari , Mohd Kasim Noor Alicezah , Al-Khateeb Alyaa , Ul-Saufie Ahmad Zia , Md Radzi Ahmad Bakhtiar , Ibrahim Khairul Shafiq , Kasim Sazzli Shahlan , Nawawi Hapizah 
Published: July 2024

AbstractMany studies have investigated the coronary risk factors (CRFs) among premature coronary artery disease (PCAD) patients. However, reports on the proportion and CRFs of PCAD according to different age cut-offs for PCAD is globally under-reported. This study aimed to determine the proportion of PCAD patients and analyse the significant CRFs according to different age cut-offs among percutaneous coronary intervention (PCI)-treated patients. Patients who underwent PCI between 2007 and 2018 in two cardiology centres were included (n = 29,241) and were grouped into four age cut-off groups that defines PCAD: (A) Males/females: < 45, (B) Males: < 50; Females: < 55, (C) Males: < 55; Females: < 60 and (D) Males: < 55; Females: < 65 years old. The average proportion of PCAD was 28%; 9.2% for group (A), 21.5% for group (B), 38.6% and 41.9% for group (C) and (D), respectively. The top three CRFs of PCAD were LDL-c level, TC level and hypertension (HTN). Malay ethnicity, smoking, obesity, family history of PCAD, TC level and history of MI were the independent predictors of PCAD across all age groups. The proportion of PCAD in Malaysia is higher compared to other studies. The most significant risk factors of PCAD are LDL-c, TC levels and HTN. Early prevention, detection and management of the modifiable risk factors are highly warranted to prevent PCAD.
The vertical mobility of the first tarsometatarsal joint during demi-plié with forced turnout in ballet dancers
Authors: Ishihara Honoka , Maeda Noriaki , Komiya Makoto , Tashiro Tsubasa , Tsutsumi Shogo , Arima Satoshi , Kawai Miki , Tamura Yuki , Ikuta Yasunari , Urabe Yukio 
Published: July 2024

AbstractThe forced turnout has a perceived risk of development of hallux valgus (HV) in ballet dancers. We determined how the forced turnout affects the sagittal mobility of the first tarsometatarsal (TMT) joint, which is one of the pathogenic factors of HV development. Seventeen female ballet dancers (body mass index: 18.2 ± 1.8 kg/m2) were included and performed demi-plié in control, functional turnout, and forced turnout conditions. Ultrasound imaging synchronized with a three-dimensional motion analysis system was used for measuring the vertical locations of the first metatarsal and medial cuneiform (MC) to evaluate the first TMT joint mobility. Plantar displacement of MC and the first TMT joint mobility in the forced turnout were the greatest among the 3 conditions. Multiple regression analysis indicated that the greater extent of the forcing angle might increase the displacement of MC and the first TMT joint mobility. Evaluating the sagittal mobility of the first TMT joint in the forced turnout can assist in understanding the association between inappropriate techniques including the forced turnout and HV development in ballet dancers. Since the excessive mobility of the first TMT joint is a factor in HV development, the acquirement of adequate active turnout may have the potential to prevent HV development in ballet dancers.
Identification of commensal gut microbiota signatures as predictors of clinical severity and disease progression in multiple sclerosis
Authors: Montgomery Theresa L. , Wang Qin , Mirza Ali , Dwyer Deanna , Wu Qi , Dowling Catherine A. , Martens Jacob W. S. , Yang Jennifer , Krementsov Dimitry N. , Mao-Draayer Yang 
Published: July 2024

AbstractMultiple sclerosis (MS) is a chronic autoimmune disease of the central nervous system and a leading cause of neurological disability in young adults. Clinical presentation and disease course are highly heterogeneous. Typically, disease progression occurs over time and is characterized by the gradual accumulation of disability. The risk of developing MS is driven by complex interactions between genetic and environmental factors, including the gut microbiome. How the commensal gut microbiota impacts disease severity and progression over time remains unknown. In a longitudinal study, disability status and associated clinical features in 58 MS patients were tracked over 4.2 ± 0.98 years, and the baseline fecal gut microbiome was characterized via 16S amplicon sequencing. Progressor status, defined as patients with an increase in Expanded Disability Status Scale (EDSS), were correlated with features of the gut microbiome to determine candidate microbiota associated with risk of MS disease progression. We found no overt differences in microbial community diversity and overall structure between MS patients exhibiting disease progression and non-progressors. However, a total of 41 bacterial species were associated with worsening disease, including a marked depletion in Akkermansia, Lachnospiraceae, and Oscillospiraceae, with an expansion of Alloprevotella, Prevotella-9, and Rhodospirillales. Analysis of the metabolic potential of the inferred metagenome from taxa associated with progression revealed enrichment in oxidative stress-inducing aerobic respiration at the expense of microbial vitamin K2 production (linked to Akkermansia), and a depletion in SCFA metabolism (linked to Oscillospiraceae). Further, as a proof of principle, statistical modeling demonstrated that microbiota composition and clinical features were sufficient to predict disease progression. Additionally, we found that constipation, a frequent gastrointestinal comorbidity among MS patients, exhibited a divergent microbial signature compared with progressor status. These results demonstrate a proof of principle for the utility of the gut microbiome for predicting disease progression in MS in a small well-defined cohort. Further, analysis of the inferred metagenome suggested that oxidative stress, vitamin K2, and SCFAs are associated with progression, warranting future functional validation and mechanistic study.
Augmented reality navigation method based on image segmentation and sensor tracking registration technology
Authors: Zhang Xiaoying , Zhu Yonggang , Chen Lumin , Duan Peng , Zhou Meijuan 
Published: July 2024

AbstractWith the rapid development of modern science and technology, navigation technology provides great convenience for people's life, but the problem of inaccurate localization in complex environments has always been a challenge that navigation technology needs to be solved urgently. To address this challenge, this paper proposes an augmented reality navigation method that combines image segmentation and multi-sensor fusion tracking registration. The method optimizes the image processing process through the GA-OTSU-Canny algorithm and combines high-precision multi-sensor information in order to achieve accurate tracking of positioning and guidance in complex environments. Experimental results show that the GA-OTSU-Canny algorithm has a faster image edge segmentation rate, and the fastest start speed is only 1.8 s, and the fastest intersection selection time is 1.2 s. The navigation system combining the image segmentation and sensor tracking and registration techniques has a highly efficient performance in real-world navigation, and its building recognition rates are all above 99%. The augmented reality navigation system not only improves the navigation accuracy in high-rise and urban canyon environments, but also significantly outperforms traditional navigation solutions in terms of navigation startup time and target building recognition accuracy. In summary, this research not only provides a new framework for the theoretical integration of image processing and multi-sensor data, but also brings innovative technical solutions for the development and application of practical navigation systems.
Evaluating regression and probabilistic methods for ECG-based electrolyte prediction
Authors: von Bachmann Philipp , Gedon Daniel , Gustafsson Fredrik K. , Ribeiro Antônio H. , Lampa Erik , Gustafsson Stefan , Sundström Johan , Schön Thomas B. 
Published: July 2024

AbstractImbalances in electrolyte concentrations can have severe consequences, but accurate and accessible measurements could improve patient outcomes. The current measurement method based on blood tests is accurate but invasive and time-consuming and is often unavailable for example in remote locations or an ambulance setting. In this paper, we explore the use of deep neural networks (DNNs) for regression tasks to accurately predict continuous electrolyte concentrations from electrocardiograms (ECGs), a quick and widely adopted tool. We analyze our DNN models on a novel dataset of over 290,000 ECGs across four major electrolytes and compare their performance with traditional machine learning models. For improved understanding, we also study the full spectrum from continuous predictions to a binary classification of extreme concentration levels. Finally, we investigate probabilistic regression approaches and explore uncertainty estimates for enhanced clinical usefulness. Our results show that DNNs outperform traditional models but model performance varies significantly across different electrolytes. While discretization leads to good classification performance, it does not address the original problem of continuous concentration level prediction. Probabilistic regression has practical potential, but our uncertainty estimates are not perfectly calibrated. Our study is therefore a first step towards developing an accurate and reliable ECG-based method for electrolyte concentration level prediction—a method with high potential impact within multiple clinical scenarios.
Prediction of anastomotic insufficiency based on the mucosal microbiome prior to colorectal surgery: a proof-of-principle study
Authors: Lehr Konrad , Lange Undine Gabriele , Hipler Noam Mathias , Vilchez-Vargas Ramiro , Hoffmeister Albrecht , Feisthammel Jürgen , Buchloh Dorina , Schanze Denny , Zenker Martin , Gockel Ines , Link Alexander , Jansen-Winkeln Boris 
Published: July 2024

AbstractAnastomotic leakage (AL) is a potentially life-threatening complication following colorectal cancer (CRC) resection. In this study, we aimed to unravel longitudinal changes in microbial structure before, during, and after surgery and to determine if microbial alterations may be predictive for risk assessment between sufficient anastomotic healing (AS) and AL prior surgery. We analysed the microbiota of 134 colon mucosal biopsies with 16S rRNA V1-V2 gene sequencing. Samples were collected from three location sites before, during, and after surgery, and patients received antibiotics after the initial collection and during surgery. The microbial structure showed dynamic surgery-related changes at different time points. Overall bacterial diversity and the abundance of some genera such as Faecalibacterium or Alistipes decreased over time, while the genera Enterococcus and Escherichia_Shigella increased. The distribution of taxa between AS and AL revealed significant differences in the abundance of genera such as Prevotella, Faecalibacterium and Phocaeicola. In addition to Phocaeicola, Ruminococcus2 and Blautia showed significant differences in abundance between preoperative sample types. ROC analysis of the predictive value of these genera for AL revealed an AUC of 0.802 (p = 0.0013). In summary, microbial composition was associated with postoperative outcomes, and the abundance of certain genera may be predictive of postoperative complications.
Urine metabolomics unravel the effects of short-term dietary interventions on oxidative stress and inflammation: a randomized controlled crossover trial
Authors: Singh Digar , Ham Dongwoo , Kim Seong-Ah , Kothari Damini , Park Yu Jin , Joung Hyojee , Lee Choong Hwan 
Published: July 2024

AbstractDietary biomarkers in urine remain elusive when evaluating diet-induced oxidative stress and inflammation. In our previous study, we conducted a randomized controlled crossover trial to compare the short-term (4-weeks) effects of the balanced Korean diet (BKD) with Western diets, including the 2010 dietary guidelines for Americans (2010 DGA) and typical American diet (TAD), on various metabolic indices in obese Korean adults. Building on this work, the current research focuses on the impact of these dietary interventions on oxidative stress (d-ROMs and BAP) and inflammation (CRP, TNF-α, IL-6, IL-1β, MCP-1) biomarkers in serum, and the concurrent urine metabolomes. Each dietary regimen was in silico and experimentally examined for their antioxidant levels using ABTS, DPPH, and FRAP assays, as well as total flavonoid (TFC) and total phenolic (TPC) contents. We assessed post-intervention variations in oxidative stress and inflammation biomarkers in serum, as well as the urine metabolite profiles for the participants (n = 48, average age: 41 years). Antioxidant contents and associated total antioxidant capacity (TAC) were significantly higher for the recommended diets (BKD and 2010 DGA) compared to TAD (p < 0.05). Butanol extracts from recommended diets (BKD and 2010 DGA) showed significantly higher antioxidant activity compared to TAD in ABTS (p < 0.01), DPPH, and FRAP (p < 0.05) assays. Consistent results were observed in total phenolic and flavonoid contents, mirroring their respective antioxidant activities. Following the intervention period, oxidative stress & inflammation markers in serum varied marginally, however, the urine metabolite profiles were clearly demarcated for the BKD and Western dietary groups (PC1 = 5.41%). For BKD group, the pre- and post-intervention urine metabolite profiles were clearly segregated (PLS2 = 2.93%). Compared to TAD, urine extracts from the recommended dietary group showed higher abundance of benzoic acid & phenolic derivatives (VIP > 0.7,p < 0.05). Metabolites associated with oxidative stress were observed higher in the urine samples from Western dietary groups compared to BKD. Urine metabolomics data delineated the post-intervention effects of three dietary interventions which corroborates the respective findings for their effects on metabolic indices.
Terahertz magnetic response of plasmonic metasurface resonators: origin and orientation dependence
Authors: Tesi Lorenzo , Hrtoň Martin , Bloos Dominik , Hentschel Mario , Šikola Tomáš , van Slageren Joris 
Published: July 2024

AbstractThe increasing miniaturization of everyday devices necessitates advancements in surface-sensitive techniques to access phenomena more effectively. Magnetic resonance methods, such as nuclear or electron paramagnetic resonance, play a crucial role due to their unique analytical capabilities. Recently, the development of a novel plasmonic metasurface resonator aimed at boosting the THz electron magnetic response in 2D materials resulted in a significant magnetic field enhancement, confirmed by both numerical simulations and experimental data. Yet, the mechanisms driving this resonance were not explored in detail. In this study, we elucidate these mechanisms using two semi-analytical models: one addressing the resonant behaviour and the other examining the orientation-dependent response, considering the anisotropy of the antennas and experimental framework. Our findings contribute to advancing magnetic spectroscopic techniques, broadening their applicability to 2D systems.