bluuebunny/crossref_metadata_2025_split huggingface dataset:
Quantitative scenarios for cascading risks in AI, climate, synthetic bio, and financial markets by 2075
Authors: Undheim Trond Arne , Ahmad Taimur 
Published: March 2024

Humanity faces a myriad of existential technology, geopolitical, and ecological risks. The paper analyzes the possibility that negative shocks superimpose and multiply their effects, leading to catastrophic macro-dynamics. Methodologically, this paper presents a rare, quantitative scenario model superimposed upon narrative scenarios where the cascading economic effects of 19 quantitative indicators of growth or decline are projected into 2075. These indicators map onto five narrative scenarios, and are subsequently re-combined to study effects of plausible cascading risk events coming to pass in the 50 years period between 2025 and 2075. Results indicate that even in the case of deeply catastrophic singular events, the world would eventually recover within 25 years, as has historically been the case. The exception is that in the event of several catastrophic events in short order around the midpoint of the 50-year scenario timeline, the cascading risk escalation would create formidable negative cascades. The possibility of a protracted depression and no visible recovery within 25 years is the result. However, if we assume a modest interaction effect, even with just 3-5 co-occurring catastrophes, the result becomes a path towards humanity’s extinction based on economic decline alone. The implications are that humanity either needs to avoid significant cascading effects at all costs or needs to identify novel ways to recover compared to previous depressions. Given the amount of model assumptions they rely upon, these projections carry a degree of uncertainty. Further study should therefore be conducted with a larger set of indicators and impacts, including mortality modeling, to assess how much worse plausible real-world outcomes might be compared to the simplified economic model deployed here.
Soil carbon dynamics in drained prairie pothole wetlands
Authors: Chizen Chantel J. , Helgason Bobbi L. , Weiseth Blake , Dhillon Gurbir Singh , Baulch Helen M. , Schoenau Jeff J. , Bedard-Haughn Angela K. 
Published: March 2024

Drainage leads to trade-offs between crop production efficiency and wetland conservation, with complex impacts on ecosystem services. In North America’s Prairie Pothole Region, wetland drainage is widespread, often to increase the available land for cultivation, prevent crop loss due to flooding, and manage soil salinity. Wetlands are known for providing key ecosystem services such as improved water quality, flood mitigation, and carbon storage. There is limited research on how changes to soil hydrology and soil redistribution through wetland drainage can impact soil carbon storage and persistence in this region. This research evaluates factors that contribute to soil carbon storage in drained prairie pothole wetland based on 33 drained wetlands in Saskatchewan, Canada. These analyses showed regional differences in the response of soil carbon storage to drainage that are driven by environmental factors such as annual precipitation, temperature, and wetland permanence. We observed increasing soil carbon storage from the Dark Brown to Black soil zones, as well as with longer wetland pond permanence. The sampling depth used for calculating soil carbon storage was especially important when comparing geographically across the soil zones as the Black soil zone had greater soil carbon stored at depth. Soil carbon was also intensively monitored over 2 years following installation of surface drainage across a wetland complex (8 drained wetlands) where water was partially directed to a consolidation wetland. We further assessed changes in soil carbon dynamics and protection from microbial decomposition based on three soil organic matter fractions, ATR-FTIR for organic matter functional groups, and phospholipid fatty acid analysis to understand the microbial community abundance and structure. After 2 years following drainage, ephemeral wetlands with short pond permanence were found to be most sensitive to drainage and the only wetland class with decreases in soil carbon. The temporary and seasonal wetland classes showed no significant differences in soil carbon content but there were changes in the organic matter with depth due to soil redistribution during drainage implementation. Jointly, this research provides region-specific estimates of soil carbon storage in drained prairie pothole wetlands that can be used to inform wetland soil carbon management in cultivated fields.
Driving factors analysis and scenario prediction of CO2 emissions in power industries of key provinces along the Yellow River based on LMDI and BP neural network
Authors: Wu Chuanbao , Sun Shuang , Cui Yingying , Xing Shuangyin 
Published: March 2024

IntroductionPower industry is one of the largest sources of CO2 emissions in China. The Yellow River Basin plays a supportive role in guaranteeing the effective supply of electricity nationwide, with numerous power generation bases. Understanding the drivers and peak of CO2 emissions of power industry in the Yellow River Basin is vital for China to fulfill its commitment to reach carbon emissions peak by 2030.MethodsThe Logarithmic Mean Divisia Index (LMDI) model was employed to explore the drivers to the change of CO2 emissions in power industries of three study areas, including Inner Mongolia Autonomous Regions, Shanxi Province, and Shandong Province in the Yellow River Basin. And Back Propagation (BP) neural network was combined with scenario analysis to empirically predict the trend of the amount of CO2 emitted by power industry (CEPI) from provincial perspective.ResultsCEPI in Inner Mongolia under the scenarios of a low degree of CO2 emissions promotion with a medium degree of CO2 emissions inhibition (LM) and a low degree of CO2 emissions promotion with a high degree of CO2 emissions inhibition (LH) scenario can reach a peak as early as 2030, with the peak value of 628.32 and 638.12 million tonnes, respectively. Moreover, in Shanxi, only CEPI under a low degree of CO2 emissions promotion scenarios (LL, LM, LH) can achieve the peak in 2025 ahead of schedule, with amounts of 319.32, 308.07, and 292.45 million tonnes. Regarding Shandong, CEPI under scenarios of a low degree of CO2 emissions promotion with a high degree of CO2 emissions inhibition (LH) and a medium degree of CO2 emissions promotion with a high degree of CO2 emissions inhibition (MH) could achieve the earliest peak time in 2025, with a peak of 434.6 and 439.36 million tonnes, respectively.DiscussionThe earliest peak time of CEPI in Shandong Province and Shanxi Province is 2025, but the peak of CEPI in Shanxi is smaller than that of Shandong. The peak time of CEPI in Inner Mongolia is relatively late, in 2030, and the peak is larger than that of the other two provinces. The per capita GDP is the most positive driving factor that contributes to the CEPI. Shandong has a strong economy, and its per capita GDP is much higher than Shanxi’s. Therefore, even under the same peak time, the CEPI in Shandong is much higher than that of Shanxi. Inner Mongolia is extensive and sparsely populated, which makes its per capita GDP rank among the top in China. In addition, Inner Mongolia’s coal-based power generation structure and high power generation also contribute to its late CO2 peak time and large CO2 peak.
Complete genome sequence of a novel Prescottella sp. R16 isolate from deep-sea sediments in the western Pacific
Authors: Ma Lingqi , Bai Yuqi , Wang Weili , Pei Shengxiang , Zhang Gaiyun 
Published: March 2024

Prescottella, a distinct genus separate from Rhodococcus, has garnered attention for its adaptability and ecological versatility. In this study, a Gram-stain positive and ovoid-rod shaped the actinobacterium strain R16 was isolated from deep-sea sediment (with a depth of 6,310 m) in the Western Pacific. On the basis of 16S rRNA gene sequence analysis, average nucleotide identity and phylogenomic analysis, strain R16 clearly represents a novel species within the genus Prescottella. Genomic analyses indicate Prescottella sp. R16 contains a circular chromosome of 4,531,251 bp with an average GC content of 68.9%, 4,208 protein-coding genes, 51 tRNA genes, and 12 rRNA operons. Additionally, four CRISPRs and 24 genomic islands are also identified. The presence of rich categories related to catalytic activity, membrane part and metabolic process highlights their involvement in cellular component, biological process, and molecular function. The genome sequence of strain R16 also revealed the presence of 13 putative biosynthetic gene clusters for secondary metabolites, including those for ε-Poly-L-lysine, ectoine, heterobactin, isorenieratene and corynecin, suggesting its potential for antibiotic production and warranting further exploration.
Nanoparticle vaccines based on the receptor binding domain of porcine deltacoronavirus elicit robust protective immune responses in mice
Authors: Wang Yuanhong , Song Junhan , Deng Xiaoying , Wang Junna , Zhang Miao , Liu Yun , Tang Pan , Liu Huili , Zhou Yanjun , Tong Guangzhi , Li Guoxin , Yu Lingxue 
Published: March 2024

BackgroundPorcine deltacoronavirus (PDCoV), a novel swine enteropathogenic coronavirus, challenges the global swine industry. Currently, there are no approaches preventing swine from PDCoV infection.MethodsA new PDCoV strain named JS2211 was isolated. Next, the dimer receptor binding domain of PDCoV spike protein (RBD-dimer) was expressed using the prokaryotic expression system, and a novel nanoparticle containing RBD-dimer and ferritin (SC-Fe) was constructed using the SpyTag/SpyCatcher system. Finally, the immunoprotection of RBD-Fe nanoparticles was evaluated in mice.ResultsThe novel PDCoV strain was located in the clade of the late Chinese isolate strains and close to the United States strains. The RBD-Fe nanoparticles were successfully established. Immune responses of the homologous prime-boost regime showed that RBD-Fe nanoparticles efficiently elicited specific humoral and cellular immune responses in mice. Notably, high level PDCoV RBD-specific IgG and neutralizing antibody (NA) could be detected, and the histopathological results showed that PDCoV infection was dramatically reduced in mice immunized with RBD-Fe nanoparticles.ConclusionThis study effectively developed a candidate nanoparticle with receptor binding domain of PDCoV spike protein that offers protection against PDCoV infection in mice.
Rapid identification of inflammatory arthritis and associated adverse events following immune checkpoint therapy: a machine learning approach
Authors: Tran Steven D. , Lin Jean , Galvez Carlos , Rasmussen Luke V. , Pacheco Jennifer , Perottino Giovanni M. , Rahbari Kian J. , Miller Charles D. , John Jordan D. , Theros Jonathan , Vogel Kelly , Dinh Patrick V. , Malik Sara , Ramzan Umar , Tegtmeyer Kyle , Mohindra Nisha , Johnson Jodi L. , Luo Yuan , Kho Abel , Sosman Jeffrey , Walunas Theresa L. 
Published: March 2024

IntroductionImmune checkpoint inhibitor-induced inflammatory arthritis (ICI-IA) poses a major clinical challenge to ICI therapy for cancer, with 13% of cases halting ICI therapy and ICI-IA being difficult to identify for timely referral to a rheumatologist. The objective of this study was to rapidly identify ICI-IA patients in clinical data and assess associated immune-related adverse events (irAEs) and risk factors.MethodsWe conducted a retrospective study of the electronic health records (EHRs) of 89 patients who developed ICI-IA out of 2451 cancer patients who received ICI therapy at Northwestern University between March 2011 to January 2021. Logistic regression and random forest machine learning models were trained on all EHR diagnoses, labs, medications, and procedures to identify ICI-IA patients and EHR codes indicating ICI-IA. Multivariate logistic regression was then used to test associations between ICI-IA and cancer type, ICI regimen, and comorbid irAEs.ResultsLogistic regression and random forest models identified ICI-IA patients with accuracies of 0.79 and 0.80, respectively. Key EHR features from the random forest model included ICI-IA relevant features (joint pain, steroid prescription, rheumatoid factor tests) and features suggesting comorbid irAEs (thyroid function tests, pruritus, triamcinolone prescription). Compared to 871 adjudicated ICI patients who did not develop arthritis, ICI-IA patients had higher odds of developing cutaneous (odds ratio [OR]=2.66; 95% Confidence Interval [CI] 1.63-4.35), endocrine (OR=2.09; 95% CI 1.15-3.80), or gastrointestinal (OR=2.88; 95% CI 1.76-4.72) irAEs adjusting for demographics, cancer type, and ICI regimen. Melanoma (OR=1.99; 95% CI 1.08-3.65) and renal cell carcinoma (OR=2.03; 95% CI 1.06-3.84) patients were more likely to develop ICI-IA compared to lung cancer patients. Patients on nivolumab+ipilimumab were more likely to develop ICI-IA compared to patients on pembrolizumab (OR=1.86; 95% CI 1.01-3.43).DiscussionOur machine learning models rapidly identified patients with ICI-IA in EHR data and elucidated clinical features indicative of comorbid irAEs. Patients with ICI-IA were significantly more likely to also develop cutaneous, endocrine, and gastrointestinal irAEs during their clinical course compared to ICI therapy patients without ICI-IA.
Aerodynamic design of a double slotted morphed flap airfoil– a numerical study
Authors: Shahrokhi S. S. , Taeibi Rahni M. , Akbari P. 
Published: March 2024

Introduction:The objective of this study is to develop and simulate a double slotted morphed flap with the intention of reducing drag and enhancing lift, thereby leading to a smaller flap size and reduced weight.Methods:A flap was meticulously designed to accommodate conditions at Mach 0.2 and Reynolds numbers of 4.7×106. To conduct the simulation, ANSYS FLUENT flow solver and POINTWISE grid generator were utilized. The morphing technique employed involved adjusting both flap mean camber and flap slots, ensuring minimal flow interferences. By discretizing the flap mean camber line, various flap geometries were achieved.Results and Discussions:The findings reveal a significant enhancement in the airfoil’s aerodynamic efficiency attributed to the implementation of the new flap design. The study shows that utilizing double-slotted morphing in the NACA 4412 airfoil at a 30° flap deflection angle increased the lift coefficient by 82% compared to the un-morphed state. A comparison of lift coefficients between this research and the NACA 4412 split flap at a 60° deflection angle indicates that the double-slotted morphing in the NACA 4412 airfoil at a smaller deflection angle of 30° results in a 14% higher maximum lift coefficient.
Development and validation of a prognostic nomogram for 3-year all-cause mortality risk among elderly patients undergoing surgery for osteoporotic fractures
Authors: Li Chong , Shi Qin , Gong Ya-qin , Zhang Ting , Lu Ke 
Published: March 2024

IntroductionTo develop and validate a comprehensive prognostic model for the mid-to-long term mortality risk among ≥50-year-old osteoporotic fracture (OPF) surgical patients.MethodsOur retrospective investigation included data from the Osteoporotic Fracture Registration System established by the Affiliated Kunshan Hospital of Jiangsu University, and involved 1,656 patients in the development set and 675 patients in the validation set. Subsequently, we employed a multivariable Cox regression model to establish a 3-year mortality predicting nomogram, and the model performance was further evaluated using C-index and calibration plots. Decision curve analysis (DCA) was employed to assess feasibility of the clinical application of this model.ResultsUsing six prognostic indexes, namely, patient age, gender, the American Society of Anesthesiologists (ASA) score, the Charlson comorbidity index (CCI), fracture site, and fracture liaison service (FLS), we generated a simple nomogram. The nomogram demonstrated satisfactory discrimination within the development (C-index = 0.8416) and validation (C-index = 0.8084) sets. Using calibration plots, we also revealed good calibration. The model successfully classified patients into different risk categories and the results were comparable in both the development and validation sets. Finally, a 1–70% probability threshold, according to DCA, suggested that the model has promise in clinical settings.ConclusionHerein, we offer a robust tool to estimating the 3-year all-cause mortality risk among elderly OPF surgical patients. However, we recommend further assessments of the proposed model prior to widespread clinical implementation.
Kinesin-7 CENP-E in tumorigenesis: Chromosome instability, spindle assembly checkpoint, and applications
Authors: Yang Yu-Hao , Wei Ya-Lan , She Zhen-Yu 
Published: March 2024

Kinesin motors are a large family of molecular motors that walk along microtubules to fulfill many roles in intracellular transport, microtubule organization, and chromosome alignment. Kinesin-7 CENP-E (Centromere protein E) is a chromosome scaffold-associated protein that is located in the corona layer of centromeres, which participates in kinetochore-microtubule attachment, chromosome alignment, and spindle assembly checkpoint. Over the past 3 decades, CENP-E has attracted great interest as a promising new mitotic target for cancer therapy and drug development. In this review, we describe expression patterns of CENP-E in multiple tumors and highlight the functions of CENP-E in cancer cell proliferation. We summarize recent advances in structural domains, roles, and functions of CENP-E in cell division. Notably, we describe the dual functions of CENP-E in inhibiting and promoting tumorigenesis. We summarize the mechanisms by which CENP-E affects tumorigenesis through chromosome instability and spindle assembly checkpoints. Finally, we overview and summarize the CENP-E-specific inhibitors, mechanisms of drug resistances and their applications.
Chinmedomics strategy for elucidating the effects and effective constituents of Danggui Buxue Decoction in treating blood deficiency syndrome
Authors: Zhang Ye , Yang Yu , Ren Junling , Yan Guangli , Yang Le , Wu Xiuhong , Kong Ling , Sun Hui , Han Ying , Zhang Xiwu , Wang Xijun 
Published: March 2024

Introduction:Danggui Buxue Decoction (DBD) is a clinically proven, effective, classical traditional Chinese medicine (TCM) formula for treating blood deficiency syndrome (BDS). However, its effects and effective constituents in the treatment of BDS remain unclear, limiting precise clinical therapy and quality control. This study aimed to accurately evaluate the effects of DBD and identify its effective constituents and quality markers.Methods:BDS was induced in rats by a combined injection of acetylphenylhydrazine and cyclophosphamide, and the efficacy of DBD against BDS was evaluated based on body weight, body temperature, energy metabolism, general status, visceral indices, histopathology, biochemical markers, and metabolomics. The effects of DBD on urinary and serum biomarkers of BDS were investigated, and the associated metabolic pathways were analyzed via metabolomics. Guided by Chinmedomics, the effective constituents and quality markers of DBD were identified by analyzing the dynamic links between metabolic biomarkers and effective constituents in vivo.Results:DBD improved energy metabolism, restored peripheral blood and serum biochemical indices, and meliorated tissue damage in rats with BDS. Correlation analyses between biochemical indices and biomarkers showed that 15(S)-HPETE, LTB4, and taurine were core biomakers and that arachidonic acid, taurine, and hypotaurine metabolism were core metabolic pathways regulated by DBD. Calycosin-7-glucoside, coumarin, ferulic acid sulfate, cycloastragenol, (Z)-ligustilide + O, astragaloside IV, acetylastragaloside I, and linoleic acid were identified as effective constituents improving the hematopoietic function of the rats in the BDS model. Additionally, calycosin-7-glucoside, ferulic acid, ligustilide, and astragaloside IV were identified as quality markers of DBD.Conclusion:The hematopoietic function of DBD was confirmed through analysis of energy metabolism, biochemical markers, histopathology, and metabolomics. Moreover, by elucidating effective constituents of DBD in BDS treatment, quality markers were confirmed using a Chinmedomics strategy. These results strengthen the quality management of DBD and will facilitate drug innovation.
Automatic assessment of disproportionately enlarged subarachnoid-space hydrocephalus from 3D MRI using two deep learning models
Authors: Yamada Shigeki , Ito Hirotaka , Matsumasa Hironori , Ii Satoshi , Otani Tomohiro , Tanikawa Motoki , Iseki Chifumi , Watanabe Yoshiyuki , Wada Shigeo , Oshima Marie , Mase Mitsuhito 
Published: March 2024

BackgroundDisproportionately enlarged subarachnoid-space hydrocephalus (DESH) is a key feature for Hakim disease (idiopathic normal pressure hydrocephalus: iNPH), but subjectively evaluated. To develop automatic quantitative assessment of DESH with automatic segmentation using combined deep learning models.MethodsThis study included 180 participants (42 Hakim patients, 138 healthy volunteers; 78 males, 102 females). Overall, 159 three-dimensional (3D) T1-weighted and 180 T2-weighted MRIs were included. As a semantic segmentation, 3D MRIs were automatically segmented in the total ventricles, total subarachnoid space (SAS), high-convexity SAS, and Sylvian fissure and basal cistern on the 3D U-Net model. As an image classification, DESH, ventricular dilatation (VD), tightened sulci in the high convexities (THC), and Sylvian fissure dilatation (SFD) were automatically assessed on the multimodal convolutional neural network (CNN) model. For both deep learning models, 110 T1- and 130 T2-weighted MRIs were used for training, 30 T1- and 30 T2-weighted MRIs for internal validation, and the remaining 19 T1- and 20 T2-weighted MRIs for external validation. Dice score was calculated as (overlapping area) × 2/total area.ResultsAutomatic region extraction from 3D T1- and T2-weighted MRI was accurate for the total ventricles (mean Dice scores: 0.85 and 0.83), Sylvian fissure and basal cistern (0.70 and 0.69), and high-convexity SAS (0.68 and 0.60), respectively. Automatic determination of DESH, VD, THC, and SFD from the segmented regions on the multimodal CNN model was sufficiently reliable; all of the mean softmax probability scores were exceeded by 0.95. All of the areas under the receiver-operating characteristic curves of the DESH, Venthi, and Sylhi indexes calculated by the segmented regions for detecting DESH were exceeded by 0.97.ConclusionUsing 3D U-Net and a multimodal CNN, DESH was automatically detected with automatically segmented regions from 3D MRIs. Our developed diagnostic support tool can improve the precision of Hakim disease (iNPH) diagnosis.