bluuebunny/crossref_metadata_2025_split huggingface dataset:
DynamicBind: predicting ligand-specific protein-ligand complex structure with a deep equivariant generative model
Authors: Lu Wei , Zhang Jixian , Huang Weifeng , Zhang Ziqiao , Jia Xiangyu , Wang Zhenyu , Shi Leilei , Li Chengtao , Wolynes Peter G. , Zheng Shuangjia 
Published: February 2024

AbstractWhile significant advances have been made in predicting static protein structures, the inherent dynamics of proteins, modulated by ligands, are crucial for understanding protein function and facilitating drug discovery. Traditional docking methods, frequently used in studying protein-ligand interactions, typically treat proteins as rigid. While molecular dynamics simulations can propose appropriate protein conformations, they’re computationally demanding due to rare transitions between biologically relevant equilibrium states. In this study, we present DynamicBind, a deep learning method that employs equivariant geometric diffusion networks to construct a smooth energy landscape, promoting efficient transitions between different equilibrium states. DynamicBind accurately recovers ligand-specific conformations from unbound protein structures without the need for holo-structures or extensive sampling. Remarkably, it demonstrates state-of-the-art performance in docking and virtual screening benchmarks. Our experiments reveal that DynamicBind can accommodate a wide range of large protein conformational changes and identify cryptic pockets in unseen protein targets. As a result, DynamicBind shows potential in accelerating the development of small molecules for previously undruggable targets and expanding the horizons of computational drug discovery.
Leukaemia exposure alters the transcriptional profile and function of BCR::ABL1 negative macrophages in the bone marrow niche
Authors: Dawson Amy , Zarou Martha M. , Prasad Bodhayan , Bittencourt-Silvestre Joana , Zerbst Désirée , Himonas Ekaterini , Hsieh Ya-Ching , van Loon Isabel , Blanco Giovanny Rodriguez , Ianniciello Angela , Kerekes Zsombor , Krishnan Vaidehi , Agarwal Puneet , Almasoudi Hassan , McCluskey Laura , Hopcroft Lisa E. M. , Scott Mary T. , Baquero Pablo , Dunn Karen , Vetrie David , Copland Mhairi , Bhatia Ravi , Coffelt Seth B. , Tiong Ong Sin , Wheadon Helen , Zanivan Sara , Kirschner Kristina , Helgason G. Vignir 
Published: February 2024

AbstractMacrophages are fundamental cells of the innate immune system that support normal haematopoiesis and play roles in both anti-cancer immunity and tumour progression. Here we use a chimeric mouse model of chronic myeloid leukaemia (CML) and human bone marrow (BM) derived macrophages to study the impact of the dysregulated BM microenvironment on bystander macrophages. Utilising single-cell RNA sequencing (scRNA-seq) of Philadelphia chromosome (Ph) negative macrophages we reveal unique subpopulations of immature macrophages residing in the CML BM microenvironment. CML exposed macrophages separate from their normal counterparts by reduced expression of the surface marker CD36, which significantly reduces clearance of apoptotic cells. We uncover aberrant production of CML-secreted factors, including the immune modulatory protein lactotransferrin (LTF), that suppresses efferocytosis, phagocytosis, and CD36 surface expression in BM macrophages, indicating that the elevated secretion of LTF is, at least partially responsible for the supressed clearance function of Ph- macrophages.
Unacceptable use of substandard metrics in policy decisions which mandate large reductions in animal-source foods
Authors: Stanton Alice V. 
Published: February 2024

AbstractMany recent very influential reports, including those from the Global Burden of Disease (GBD) Risk Factor Collaborators, the EAT-Lancet Commission on Food, Planet, Health, and the Lancet Countdown on Health and Climate Change, have recommended dramatic reductions or total exclusion of animal-source foods, particularly ruminant products (red meat and dairy), from the human diet. They strongly suggest that these dietary shifts will not only benefit planetary health but also human health. However, as detailed in this perspective, there are grounds for considerable concern in regard to the quality and transparency of the input data, the validity of the assumptions, and the appropriateness of the statistical modelling, used in the calculation of the global health estimates, which underpin the claimed human health benefits. The lessor bioavailability of protein and key micronutrients from plant-source foods versus animal-source foods was not adequately recognised nor addressed in any of these reports. Furthermore, assessments of bias and certainty were either limited or absent. Despite many of these errors and limitations being publically acknowledged by the GBD and the EAT-Lancet authors, no corrections have been applied to the published papers. As a consequence, these reports continue to erroneously influence food policy decisions and international dietary guidelines, such as the World Wildlife Fund’s Livewell Diet, and the Nordic Nutrition Recommendations 2023.
Whole-genome variants dataset of 209 local chickens from China
Authors: Tan Xiaodong , Zhang Jiawen , Dong Jie , Huang Minjie , Li Qinghai , Wang Huanhuan , Bai Lijuan , Cui Ming , Zhou Zhenzhen , Yang Shuyuan , Wang Deqian 
Published: February 2024

AbstractCompared to commercial chickens, local breeds exhibit better in meat quality and flavour, but the productivity (e.g., growth rate, body weight) of local chicken breeds is rather low. Genetic analysis based on whole-genome sequencing contributes to elucidating the genetic markers or putative candidate genes related to some economic traits, facilitating the improvement of production performance, the acceleration of breeding progress, and the conservation of genetic resources. Here, a total of 209 local chickens from 13 breeds were investigated, and the observation of approximately 91.4% high-quality sequences (Q30 > 90%) and a mapping rate over 99% for each individual indicated good results of this study, as confirmed by a genome coverage of 97.6%. Over 19 million single nucleotide polymorphisms (SNPs) and 1.98 million insertion-deletions (InDels) were identified using the reference genome (GRCg7b), further contributing to the public database. This dataset provides valuable resources for studying genetic diversity and adaptation and for the cultivation of new chicken breeds/lines.
Digital economy development boosts urban resilience—evidence from China
Authors: Wang Haohui , Peng Gang , Du Hongmei 
Published: February 2024

AbstractFocusing on the impact of the digital economy on urban resilience is beneficial to the sustainable development of cities. This paper empirically examines the impact of digital economic development on urban resilience and its mechanisms by measuring urban resilience and the level of urban digital economy with the entropy-weighted TOPSIS method using the data of 252 Chinese cities from 2011 to 2020. The findings show that digital economic development effectively promotes urban resilience at the 1% significance level, and this conclusion remains valid after a series of endogeneity and robustness tests. The channel mechanism suggests that the development of the digital economy can improve urban resilience by optimizing urban distributional effects and promoting the upgrading of urban industrial structures. This paper discusses the nonlinear relationship between the two using the MMQR model and the threshold model. The results show that urban resilience development level is in a higher quartile of cities, and digital economy development has a greater impact on urban resilience improvement. Meanwhile, there are two threshold values for the nonlinear impact of the digital economy on urban resilience, which are 0.026 and 0.082, respectively. Further, the spatial effect between the two is also verified. From the perspective of heterogeneity analysis, the digital economy development of high-class cities, key city clusters, and cities in eastern and western regions has a greater effect on urban resilience. This study can provide ideas and inspiration for countries to enhance urban resilience and promote sustainable urban development through the development of the digital economy.
Techno-economic optimization for isolated hybrid PV/wind/battery/diesel generator microgrid using improved salp swarm algorithm
Authors: Belboul Zakaria , Toual Belgacem , Bensalem Abderrahman , Ghenai Chaouki , Khan Baseem , Kamel Salah 
Published: February 2024

AbstractThe main objective of this study is to develop a new method for solving the techno-economic optimization problem of an isolated microgrid powered by renewable energy sources like solar panels, wind turbines, batteries, and diesel generators while minimizing greenhouse gas emissions. An Improved Salp Swarm Algorithm (ISSA) with a position adaptation mechanism for the salp leader that involves a leader salp that moves about depending on both food availability and its previous position has been proposed to overcome the convergence problem. In the original SSA, as the approach converges, it can no longer find optimal solutions and becomes trapped in a local minimum. Three Microgrid System (MS) configurations are discussed: PV/WT/BESU/DG, PV/BESU/DG, and WT/BESU/DG. The proposed method seeks to find a middle ground between technical criteria and environmental concerns when deciding on PV, WT, BESU, and DG sizes. The findings indicate that the proposed ISSA approach gives superior results compared to other well-known algorithms like the original SSA, the Ant Lion Optimizer (ALO), the Dragonfly Approach (DA), and the Moth-Flame Optimization Algorithm (MFO), which, after significant investigation, has been proven to help determine the appropriate microgrid size. With PV sizes of 10, 9 WT, 24 BESU, and 3 DG, the PV/WT/BESU/DG configuration offers the highest level of cost-effectiveness with Cost of Energy (COE) of 0.2109 $/kWh, Net Present Cost (NPC) of 376,063.8 $, Loss of Power Supply Probability (LPSP) of 4%, Renewable Energy Fraction (REF) of 96%, and CO2 emission of 12.4457 tons/year. ISSA is brought up as a possible solution to both the problem of rising energy prices and the difficulties inherent in microgrid design.
Characterising the behaviours in most severe and least severe emotional outbursts in young people
Authors: Shenton Benjamin Daniel Kitchen , Chung Justin Cheuk Yin , Woodcock Kate Anne 
Published: February 2024

AbstractEmotional outbursts are displays of intense, challenging behaviour and are prevalent in individuals with neurodevelopmental disorders. Outbursts present a danger to individuals and their carers and are cited as reasons for referral to mental health services. However, it is currently unclear how the characteristics of outbursts may determine their severity. Carers (n = 214) of individuals aged between 6 and 25 and experiencing outbursts at least once per month completed the Emotional Outburst Questionnaire. Questionnaire items were used to compare behaviours observed in most severe and least severe outbursts through quantitative and content analyses of open ended data. Signs of physiological arousal and aggression were seen significantly more in most severe outbursts compared to least severe outbursts. Least severe outbursts were seen more frequently, but most severe outbursts were reported to have a longer duration, be at a higher intensity, and have a longer recovery time. Additionally, associations were found between reduced eye contact and most severe outbursts, as well as expression of suicidal ideation and most severe outbursts. Certain behaviours, notably forms of aggression and physiological arousal, are associated with most severe outbursts. Findings of this study may allow future work examining cross-disorder differences in outbursts to inform targeted interventions aiming to reduce outburst severity and impact. Additionally, identification of such outburst characteristics could aid in measurement of outburst severity, which would allow for more reliable and valid studies on outburst interventions.
Uncovering the clinicopathological features of early recurrence after surgical resection of pancreatic cancer
Authors: Chon Hye Yeon , Lee Hee Seung , Sung You-Na , Tae Yoo Keung , Park Chan Hee , Leem Galam , Kim So Jung , Jo Jung Hyun , Chung Moon Jae , Park Jeong Youp , Park Seung Woo , Hong Seung-Mo , Bang Seungmin 
Published: February 2024

AbstractTo identify risk factors and biomarker for early recurrence in patients diagnosed with pancreatic cancer who undergo curative resection. Early recurrence after curative resection of pancreatic cancer is an obstacle to long-term survival. We retrospectively reviewed 162 patients diagnosed with pancreatic cancer who underwent curative resection. Early recurrence was defined as recurrence within 12 months of surgery. We selected S100A2 as a biomarker and investigated its expression using immunohistochemistry. Of the total, 79.6% (n = 129) of patients received adjuvant chemotherapy after surgery and 117 (72.2%) experienced recurrence, of which 73 (45.1%) experience early recurrence. In multivariate analysis, age < 60 years, presence of lymph node metastasis, and no adjuvant chemotherapy were significantly associated with early recurrence (all P < 0.05). The proportion of patients with high S100A2 expression (H-score > 5) was significantly lower in the early recurrence group (41.5% vs. 63.3%, P = 0.020). The cumulative incidence rate of early recurrence was higher in patients with an S100A2 H-score < 5 (41.5% vs. 63.3%, P = 0.012). The median overall survival of patients with higher S100A2 expression was longer than those with lower S100A2 expression (median 30.1 months vs. 24.2 months, P = 0.003). High-risk factors for early recurrence after surgery for pancreatic cancer include young age, lymph node metastasis, and no adjuvant therapy. Neoadjuvant treatment or intensive adjuvant therapy after surgery may improve the prognosis of patients with high-risk signatures. In patients who receive adjuvant therapy, high S100A2 expression is a good predictor.
Deep learning forecasting of large induced earthquakes via precursory signals
Authors: Convertito Vincenzo , Giampaolo Fabio , Amoroso Ortensia , Piccialli Francesco 
Published: February 2024

AbstractPrecursory phenomena to earthquakes have always attracted researchers’ attention. Among the most investigated precursors, foreshocks play a key role. However, their prompt identification with respect to background seismicity still remains an issue. The task is worsened when dealing with low-magnitude earthquakes. Despite that, seismology and, in particular real-time seismology, can nowadays benefit from the use of Artificial Intelligence (AI) to face the challenge of effective precursory signals discrimination. Here, we propose a deep learning method named PreD-Net (precursor detection network) to address precursory signal identification of induced earthquakes. PreD-Net has been trained on data related to three different induced seismicity areas, namely The Geysers, located in California, USA, Cooper Basin, Australia, Hengill in Iceland. The network shows a suitable model generalization, providing considerable results on samples that were not used during the network training phase of all the sites. Tests on related samples of induced large events, with the addition of data collected from the Basel catalogue, Switzerland, assess the possibility of building a real-time warning strategy to be used to avoid adverse consequences during field operations.
Cell-free DNA methylation analysis as a marker of malignancy in pleural fluid
Authors: Bixby Billie , Vrba Lukas , Lenka Jyoti , Oshiro Marc M. , Watts George S. , Hughes Trina. , Erickson Heidi , Chopra Madhav , Knepler James L. , Knox Kenneth S. , Jarnagin Lisa , Alalawi Raed , Kala Mrinalini , Bernert Richard , Routh Joshua , Roe Denise J. , Garland Linda L. , Futscher Bernard W. , Nelson Mark A. 
Published: February 2024

AbstractDiagnosis of malignant pleural effusion (MPE) is made by cytological examination of pleural fluid or histological examination of pleural tissue from biopsy. Unfortunately, detection of malignancy using cytology has an overall sensitivity of 50%, and is dependent upon tumor load, volume of fluid assessed, and cytopathologist experience. The diagnostic yield of pleural fluid cytology is also compromised by low abundance of tumor cells or when morphology is obscured by inflammation or reactive mesothelial cells. A reliable molecular marker that may complement fluid cytology for the diagnosis of malignant pleural effusion is needed. The purpose of this study was to establish a molecular diagnostic approach based on pleural effusion cell-free DNA methylation analysis for the differential diagnosis of malignant pleural effusion and benign pleural effusion. This was a blind, prospective case–control biomarker study. We recruited 104 patients with pleural effusion for the study. We collected pleural fluid from patients with: MPE (n = 48), indeterminate pleural effusion in subjects with known malignancy or IPE (n = 28), and benign PE (n = 28), and performed the Sentinel-MPE liquid biopsy assay. The methylation level of Sentinel-MPE was markedly higher in the MPE samples compared to BPE control samples (p < 0.0001) and the same tendency was observed relative to IPE (p = 0.004). We also noted that the methylation signal was significantly higher in IPE relative to BPE (p < 0.001). We also assessed the diagnostic efficiency of the Sentinel-MPE test by performing receiver operating characteristic analysis (ROC). For the ROC analysis we combined the malignant and indeterminate pleural effusion groups (n = 76) and compared against the benign group (n = 28). The detection sensitivity and specificity of the Sentinel-MPE test was high (AUC = 0.912). The Sentinel-MPE appears to have better performance characteristics than cytology analysis. However, combining Sentinel-MPE with cytology analysis could be an even more effective approach for the diagnosis of MPE. The Sentinel-MPE test can discriminate between BPE and MPE. The Sentinel-MPE liquid biopsy test can detect aberrant DNA in several different tumor types. The Sentinel-MPE test can be a complementary tool to cytology in the diagnosis of MPE.
Categorization of disaster-related deaths in Minamisoma city after the Fukushima nuclear disaster using clustering analysis
Authors: Yoshimura Hiroki , Sawano Toyoaki , Murakami Michio , Uchi Yuna , Kawashima Moe , Kitazawa Kemmei , Nonaka Saori , Ito Naomi , Saito Hiroaki , Abe Toshiki , Moriyama Nobuaki , Sakakibara Mamoru , Yagiuchi Kazuko , Otsuki Mako , Hori Arinobu , Ozaki Akihiko , Yamamoto Chika , Zhao Tianchen , Uchiyama Taiga , Oikawa Tomoyoshi , Niwa Shinichi , Tsubokura Masaharu 
Published: February 2024

AbstractThe medical situation during disasters often differs from that at usual times. Disasters can lead to significant mortality that can be difficult to monitor. The types of disaster-related deaths are largely unknown. In this study, we conducted a survey to categorize the disaster-related deaths caused by a radiation disaster. A total of 520 people living in Minamisoma City, Fukushima Prefecture, at the time of the Fukushima Daiichi Nuclear Power Plant accident, who were certified to have died due to disaster-related causes were surveyed. We divided the participants into those who were at home at the time of the earthquake and those who were in hospitals or facilities when the disaster struck and conducted a hierarchical cluster analysis of the two groups. Disaster-related deaths could be divided into seven groups for those who were at home at the time of the disaster and five groups for those who were in hospitals or facilities at the time of the disaster. Each group showed different characteristics, such as "the group with disabilities," "the group receiving care," and "the group with depression," and it became evident that not only uniform post-disaster support, but support tailored to the characteristics of each group is necessary.
Long-term use of anti-cholesterol drugs and cancer risks in a Japanese population
Authors: Okita Yuki , Sobue Tomotaka , Zha Ling , Kitamura Tetsuhisa , Iwasaki Motoki , Inoue Manami , Yamaji Taiki , Tsugane Shoichiro , Sawada Norie 
Published: February 2024

AbstractSeveral studies have investigated the association between the use of anti-cholesterol drugs and cancer risks, of which results have been inconsistent. This study included 67,768 participants from the Japan Public Health Center-based Prospective Study. The data on anti-cholesterol drug use was collected using three questionnaires of the survey conducted every five years. We divided the participants into three groups according to the duration of the anti-cholesterol drug use. Multivariable-adjusted Cox proportional hazard regression models were used to calculate hazard ratios (HR) and 95% confidence intervals (CI). During the 893,009 person-years of follow-up from the 10-year follow-up survey, 8,775 participants (5,387 men and 3,388 women) were newly diagnosed with cancers. The duration of anti-cholesterol drug use was significantly associated with a decreased risk of liver cancer (HR:0.26, 95% CI 0.11–0.64 in > 5 y group) and with an increased risk of pancreatic cancer (HR:1.59, 95% CI 1.03–2.47 in > 5 y group). Moreover, a different trend was observed between men and women in the association with the risk of lung cancer. This study suggested that long-term use of anti-cholesterol drugs may have associations with a decreased incidence of liver cancer and with an increased incidence of pancreatic cancers.
Attitudes towards AI: measurement and associations with personality
Authors: Stein Jan-Philipp , Messingschlager Tanja , Gnambs Timo , Hutmacher Fabian , Appel Markus 
Published: February 2024

AbstractArtificial intelligence (AI) has become an integral part of many contemporary technologies, such as social media platforms, smart devices, and global logistics systems. At the same time, research on the public acceptance of AI shows that many people feel quite apprehensive about the potential of such technologies—an observation that has been connected to both demographic and sociocultural user variables (e.g., age, previous media exposure). Yet, due to divergent and often ad-hoc measurements of AI-related attitudes, the current body of evidence remains inconclusive. Likewise, it is still unclear if attitudes towards AI are also affected by users’ personality traits. In response to these research gaps, we offer a two-fold contribution. First, we present a novel, psychologically informed questionnaire (ATTARI-12) that captures attitudes towards AI as a single construct, independent of specific contexts or applications. Having observed good reliability and validity for our new measure across two studies (N1 = 490; N2 = 150), we examine several personality traits—the Big Five, the Dark Triad, and conspiracy mentality—as potential predictors of AI-related attitudes in a third study (N3 = 298). We find that agreeableness and younger age predict a more positive view towards artificially intelligent technology, whereas the susceptibility to conspiracy beliefs connects to a more negative attitude. Our findings are discussed considering potential limitations and future directions for research and practice.