bluuebunny/crossref_metadata_2025_split huggingface dataset:
Exploring interactions between socioeconomic context and natural hazards on human population displacement
Authors: Ronco Michele , Tárraga José María , Muñoz Jordi , Piles María , Marco Eva Sevillano , Wang Qiang , Espinosa Maria Teresa Miranda , Ponserre Sylvain , Camps-Valls Gustau 
Published: December 2023

AbstractClimate change is leading to more extreme weather hazards, forcing human populations to be displaced. We employ explainable machine learning techniques to model and understand internal displacement flows and patterns from observational data alone. For this purpose, a large, harmonized, global database of disaster-induced movements in the presence of floods, storms, and landslides during 2016–2021 is presented. We account for environmental, societal, and economic factors to predict the number of displaced persons per event in the affected regions. Here we show that displacements can be primarily attributed to the combination of poor household conditions and intense precipitation, as revealed through the interpretation of the trained models using both Shapley values and causality-based methods. We hence provide empirical evidence that differential or uneven vulnerability exists and provide a means for its quantification, which could help advance evidence-based mitigation and adaptation planning efforts.
Artificial cell synthesis using biocatalytic polymerization-induced self-assembly
Authors: Belluati Andrea , Jimaja Sètuhn , Chadwick Robert J. , Glynn Christopher , Chami Mohamed , Happel Dominic , Guo Chao , Kolmar Harald , Bruns Nico 
Published: December 2023

AbstractArtificial cells are biomimetic microstructures that mimic functions of natural cells, can be applied as building blocks for molecular systems engineering, and host synthetic biology pathways. Here we report enzymatically synthesized polymer-based artificial cells with the ability to express proteins. Artificial cells were synthesized using biocatalytic atom transfer radical polymerization-induced self-assembly, in which myoglobin synthesizes amphiphilic block co-polymers that self-assemble into structures such as micelles, worm-like micelles, polymersomes and giant unilamellar vesicles (GUVs). The GUVs encapsulate cargo during the polymerization, including enzymes, nanoparticles, microparticles, plasmids and cell lysate. The resulting artificial cells act as microreactors for enzymatic reactions and for osteoblast-inspired biomineralization. Moreover, they can express proteins such as a fluorescent protein and actin when fed with amino acids. Actin polymerizes in the vesicles and alters the artificial cells’ internal structure by creating internal compartments. Thus, biocatalytic atom transfer radical polymerization-induced self-assembly-derived GUVs can mimic bacteria as they are composed of a microscopic reaction compartment that contains genetic information for protein expression upon induction.
Recent irreversible retreat phase of Pine Island Glacier
Authors: Reed Brad , Green J. A. Mattias , Jenkins Adrian , Gudmundsson G. Hilmar 
Published: December 2023

AbstractPine Island Glacier (PIG), a part of the West Antarctic marine ice sheet, has recently undergone substantial changes including speed up, retreat and thinning. Theoretical arguments and modelling work suggest that marine ice sheets can become unstable and undergo irreversible retreat. Here, we use an ice-flow model validated by observational data to show that a rapid PIG retreat in the 1970s from a subglacial ridge to an upstream ice plain was self-enhancing and irreversible. The results suggest that by the early 1970s, the retreat of PIG had reached a point beyond which its original position at the ridge could not be recovered, even during subsequent periods of cooler ocean conditions. The irreversible phase ended by the early 1990s after almost 40 km of retreat and 0.34 mm added to global mean sea level, making PIG the main contributor from the Antarctic ice sheet in this period.
Local cooling and drying induced by Himalayan glaciers under global warming
Authors: Salerno Franco , Guyennon Nicolas , Yang Kun , Shaw Thomas E. , Lin Changgui , Colombo Nicola , Romano Emanuele , Gruber Stephan , Bolch Tobias , Alessandri Andrea , Cristofanelli Paolo , Putero Davide , Diolaiuti Guglielmina , Tartari Gianni , Verza Gianpietro , Thakuri Sudeep , Balsamo Gianpaolo , Miles Evan S. , Pellicciotti Francesca 
Published: December 2023

AbstractUnderstanding the response of Himalayan glaciers to global warming is vital because of their role as a water source for the Asian subcontinent. However, great uncertainties still exist on the climate drivers of past and present glacier changes across scales. Here, we analyse continuous hourly climate station data from a glacierized elevation (Pyramid station, Mount Everest) since 1994 together with other ground observations and climate reanalysis. We show that a decrease in maximum air temperature and precipitation occurred during the last three decades at Pyramid in response to global warming. Reanalysis data suggest a broader occurrence of this effect in the glacierized areas of the Himalaya. We hypothesize that the counterintuitive cooling is caused by enhanced sensible heat exchange and the associated increase in glacier katabatic wind, which draws cool air downward from higher elevations. The stronger katabatic winds have also lowered the elevation of local wind convergence, thereby diminishing precipitation in glacial areas and negatively affecting glacier mass balance. This local cooling may have partially preserved glaciers from melting and could help protect the periglacial environment.
Antarctic Peninsula glaciation patterns set by landscape evolution and dynamic topography
Authors: Fox Matthew , Clinger Anna , Smith Adam G. G. , Cuffey Kurt , Shuster David , Herman Frederic 
Published: December 2023

AbstractThe dimensions of past ice sheets provide a record of palaeoclimate but depend on underlying topography, which evolves over geological timescales by tectonic uplift and erosional downcutting. Erosion during the Pleistocene epoch (2,580 to 11.650 thousand years ago) reduced glacier extent in some locations even as climate cooled, but whether other non-climatic influences impacted the glacial–geological record is poorly known. The Antarctic Peninsula provides an opportunity to examine this issue because of its long glacial history and preservation of remnants of a low-relief pre-glacial land surface. Here we reconstructed both palaeo-surface topography and long-wavelength variations of surface uplift for the Antarctic Peninsula by using inverse analysis that assimilates local topographic remnants with the branching structures of entire modern drainage networks. We found that the Antarctic Peninsula rose tectonically by up to 1.5 km due to dynamical support from the mantle. Glaciological models using the current climate and our palaeotopography show greatly reduced ice extent in the northern Antarctic Peninsula compared with modern, indicating that the onset of glaciation identified at offshore sites reflects tectonic uplift of the topography rather than climatic cooling. In the southern Antarctic Peninsula, however, we suggest the low-relief pre-glacial landscape supported a considerably greater ice volume than the modern mountainous topography, illustrating the influence of erosional sculpting on glaciation patterns.
Next-Generation Intensity-Duration-Frequency Curves for Diverse Land across the Continental United States
Authors: Yan Hongxiang , Duan Zhuoran , Wigmosta Mark S. , Sun Ning , Gutmann Ethan D. , Kruyt Bert , Arnold Jeffrey R. 
Published: December 2023

AbstractThe current methods for designing hydrological infrastructure rely on precipitation-based intensity-duration-frequency curves. However, they cannot accurately predict flooding caused by snowmelt or rain-on-snow events, potentially leading to underdesigned infrastructure and property damage. To address these issues, next-generation intensity-duration-frequency (NG-IDF) curves have been developed for the open condition, characterizing water available for runoff from rainfall, snowmelt, and rain-on-snow. However, they lack consideration of land use land cover (LULC) factors, which can significantly affect runoff processes. We address this limitation by expanding open area NG-IDF dataset to include eight vegetated LULCs over the continental United States, including forest (deciduous, evergreen, mixed), shrub, grass, pasture, crop, and wetland. This NG-IDF 2.0 dataset offers a comprehensive analysis of hydrological extreme events and their associated drivers under different LULCs at a continental scale. It will serve as a useful resource for improving standard design practices and aiding in the assessment of infrastructure design risks. Additionally, it provides useful insights into how changes in LULC impact flooding magnitude, mechanisms, timing, and snow water supply.
A causative role for periarticular skeletal muscle weakness in the progression of joint damage and pain in OA
Authors: Kim Ju-Ryoung , Pham Thi Hong Nhung , Kim Wan-Uk , Kim Hyun Ah 
Published: December 2023

AbstractAlthough osteoarthritis (OA) is regarded as a disease of the articular cartilage, recent research has demonstrated alterations in periarticular muscles that surround the affected joint. Here, we investigated changes in periarticular muscle during the progression of OA, as well as the cause-and-effect relationship between muscle weakness and OA, in a mouse model of OA by destabilization of the medial meniscus (DMM). Pathological phenotypes in the periarticular muscles were assessed in the early and late stages of OA by DMM. OA pathology and pain behavior in the mice after DMM induction were examined in response to periarticular muscle weakness induced by multiple rounds of barium chloride (BaCl2) injections. The examinations were also performed in myostatin knockout mice with strengthened muscle phenotypes by muscle hypertrophy. Morphological alterations in the tibialis anterior (TA) and quadriceps muscles in DMM mice included variations in muscle-fiber size, aberrant extracellular matrix (ECM) deposition, inflammatory cell infiltration, and decreased muscle mass. Periarticular muscle fibers isolated from DMM mice showed reductions in the number of satellite cells and myogenic capacity of primary myoblast, as well as proliferation. DMM + muscle injury mice also showed exacerbated joint degeneration compared to the DMM vehicles. Myostatin knockout mice were characterized by attenuated OA and the complete abrogation of pain behavior after DMM. Our results suggest an association between muscle weakness and OA progression and pain.
Deviation-support based fuzzy ensemble of multi-modal deep learning classifiers for breast cancer prognosis prediction
Authors: Arya Nikhilanand , Saha Sriparna 
Published: December 2023

AbstractBreast cancer is the fifth leading cause of death in females worldwide. Early detection and treatment are crucial for improving health outcomes and preventing more serious conditions. Analyzing diverse information from multiple sources without errors, particularly with the growing burden of cancer cases, is a daunting task for humans. In this study, our main objective is to improve the accuracy of breast cancer survival prediction using a novel ensemble approach. It is novel due to the consideration of deviation (closeness between predicted classes and actual classes) and support (sparsity between predicted classes and actual classes) of the predicted class with respect to the actual class, a feature lacking in traditional ensembles. The ensemble uses fuzzy integrals on support and deviation scores from base classifiers to calculate aggregated scores while considering how confident or uncertain each classifier is. The proposed ensemble mechanism has been evaluated on a multi-modal breast cancer dataset of breast tumors collected from participants in the METABRIC trial. The proposed architecture proves its efficiency by achieving the accuracy, sensitivity, F1-score, and balanced accuracy of 82.88%, 58.64%, 62.94%, and 74.75% respectively. The obtained results are superior to the performance of individual classifiers and existing ensemble approaches.
Urinary exosomal miRNA signature of IgA nephropathy: a case–control study
Authors: Shankar Mythri , Shetty Aditya , N.S. Madhura , C.G. Sreedhara , A. Kishan , Tennankore Karthik 
Published: December 2023

AbstractIgA nephropathy is the most common primary glomerulonephritis worldwide and can progress to end-stage kidney disease (ESKD). The current “gold standard” for diagnosis is kidney biopsy, which is invasive and associated with morbidity. miRNAs are small, non-coding endogenous RNA that may serve as non-invasive biomarkers, and that are found in urinary exosomes. Thus far, there is a paucity of studies of the miRNA profile for the diagnosis of IgA nephropathy. Hence, we aimed to study the urinary exosomal miRNA signature of Indian patients with IgA nephropathy. Fifty biopsy-proven IgA nephropathy patients, 50 healthy controls and 25 patients with ESKD (IgA nephropathy) were recruited over 2 years (2020–2022). Urinary exosomes were isolated from which miRNA was extracted . Analysis of urinary exosomal miRNA was done using the digital multiplexed nCounter® human v3 miRNA Expression Assay which contains 799 unique miRNA barcodes. Candidate miRNAs were identified using Lasso regression and consensus clustering. The mean age of IgA nephropathy patients was 36.32 ± 3.067 years, mean creatinine was 2.26 ± 0.318 mg/dl and mean proteinuria was 2.69 ± 0.64 g/day. Compared to healthy controls, the majority (N = 150) of miRNAs were significantly downregulated. Five candidate miRNAs (hsa.miR.146b.3p, hsa.miR.599, hsa.miR.4532, hsa.miR.664b.5p and hsa.miR.221.5p) were able to differentiate between IgA nephropathy cases and controls (AUC > 0.90); the presence of all 5 was associated with 100% specificity and sensitivity for diagnosing IgA nephropathy cases. This study of Indian patients identified that there was a significant difference in the urinary exosomal miRNA profile between IgA nephropathy cases and healthy controls, suggesting that miRNAs may be valuable in the non-invasive diagnosis of IgA nephropathy.
Optimizing supplemental light spectrum improves growth and yield of cut roses
Authors: Davarzani Maryam , Aliniaeifard Sasan , Mehrjerdi Mahboobeh Zare , Roozban Mahmood Reza , Saeedi Seyyed Arash , Gruda Nazim S. 
Published: December 2023

AbstractDuring the seasons with limited light intensity, reductions in growth, yield, and quality are challenging for commercial cut rose production in greenhouses. Using artificial supplemental light is recommended for maintaining commercial production in regions with limited light intensity. Nowadays, replacing traditional lighting sources with LEDs attracted lots of attention. Since red (R) and blue (B) light spectra present the important wavelengths for photosynthesis and growth, in the present study, different ratios of supplemental R and B lights, including 90% R: B 10% (R90B10), 80% R: 20% B (R80B20), 70% R: 30% B (R70B30) with an intensity of 150 µmol m−2 s−1 together with natural light and without supplemental light (control) were applied on two commercial rose cultivars. According to the obtained results, supplemental light improved growth, carbohydrate levels, photosynthesis capacity, and yield compared to the control. R90B10 in both cultivars reduced the time required for flowering compared to the control treatment. R90B10 and R80B20 obtained the highest number of harvested flower stems in both cultivars. Chlorophyll and carotenoid levels were the highest under control. They had a higher ratio of B light, while carbohydrate and anthocyanin contents increased by having a high ratio of R light in the supplemental light. Analysis of chlorophyll fluorescence was indicative of better photosynthetic performance under a high ratio of R light in the supplemental light. In conclusion, the R90B10 light regime is recommended as a suitable supplemental light recipe to improve growth and photosynthesis, accelerate flowering, and improve the yield and quality of cut roses.
Normative tDCS over V5 and FEF reveals practice-induced modulation of extraretinal smooth pursuit mechanisms, but no specific stimulation effect
Authors: Radecke Jan-Ole , Sprenger Andreas , Stöckler Hannah , Espeter Lisa , Reichhardt Mandy-Josephine , Thomann Lara S. , Erdbrügger Tim , Buschermöhle Yvonne , Borgwardt Stefan , Schneider Till R. , Gross Joachim , Wolters Carsten H. , Lencer Rebekka 
Published: December 2023

AbstractThe neural networks subserving smooth pursuit eye movements (SPEM) provide an ideal model for investigating the interaction of sensory processing and motor control during ongoing movements. To better understand core plasticity aspects of sensorimotor processing for SPEM, normative sham, anodal or cathodal transcranial direct current stimulation (tDCS) was applied over visual area V5 and frontal eye fields (FEF) in sixty healthy participants. The identical within-subject paradigm was used to assess SPEM modulations by practice. While no specific tDCS effects were revealed, within- and between-session practice effects indicate plasticity of top-down extraretinal mechanisms that mainly affect SPEM in the absence of visual input and during SPEM initiation. To explore the potential of tDCS effects, individual electric field simulations were computed based on calibrated finite element head models and individual functional localization of V5 and FEF location (using functional MRI) and orientation (using combined EEG/MEG) was conducted. Simulations revealed only limited electric field target intensities induced by the applied normative tDCS montages but indicate the potential efficacy of personalized tDCS for the modulation of SPEM. In sum, results indicate the potential susceptibility of extraretinal SPEM control to targeted external neuromodulation (e.g., personalized tDCS) and intrinsic learning protocols.
Metagenomic sequencing detects human respiratory and enteric viruses in air samples collected from congregate settings
Authors: Minor Nicholas R. , Ramuta Mitchell D. , Stauss Miranda R. , Harwood Olivia E. , Brakefield Savannah F. , Alberts Alexandra , Vuyk William C. , Bobholz Max J. , Rosinski Jenna R. , Wolf Sydney , Lund Madelyn , Mussa Madison , Beversdorf Lucas J. , Aliota Matthew T. , O’Connor Shelby L. , O’Connor David H. 
Published: December 2023

AbstractInnovative methods for evaluating virus risk and spread, independent of test-seeking behavior, are needed to improve routine public health surveillance, outbreak response, and pandemic preparedness. Throughout the COVID-19 pandemic, environmental surveillance strategies, including wastewater and air sampling, have been used alongside widespread individual-based SARS-CoV-2 testing programs to provide population-level data. These environmental surveillance strategies have predominantly relied on pathogen-specific detection methods to monitor viruses through space and time. However, this provides a limited picture of the virome present in an environmental sample, leaving us blind to most circulating viruses. In this study, we explore whether pathogen-agnostic deep sequencing can expand the utility of air sampling to detect many human viruses. We show that sequence-independent single-primer amplification sequencing of nucleic acids from air samples can detect common and unexpected human respiratory and enteric viruses, including influenza virus type A and C, respiratory syncytial virus, human coronaviruses, rhinovirus, SARS-CoV-2, rotavirus, mamastrovirus, and astrovirus.
Uncovering the factors that affect earthquake insurance uptake using supervised machine learning
Authors: Ng’ombe John N. , Addai Kwabena Nyarko , Mzyece Agness , Han Joohun , Temoso Omphile 
Published: December 2023

AbstractThe escalating threat of natural disasters to public safety worldwide underlines the crucial role of effective environmental risk management tools, such as insurance. This is particularly evident in the case of earthquakes that occurred in Oklahoma between 2011 and 2020, which were linked to wastewater injection, underscoring the need for earthquake insurance. In this regard, from a survey of 812 respondents in Oklahoma, USA, we used supervised machine learning techniques (i.e., logit, ridge, least absolute shrinkage and selection operator (LASSO), decision tree, and random forest classifiers) to identify the factors that influence earthquake insurance uptake and to predict individuals who would acquire earthquake insurance. Our findings reveal that influential factors that affect earthquake insurance uptake include demographic factors such as older age, male gender, race, and ethnicity. These were found to significantly influence the decision to purchase earthquake insurance. Additionally, individuals residing in rental properties were less likely to purchase earthquake insurance, while longer residency in Oklahoma had a positive influence. Past experience of earthquakes was also found to positively influence the decision to purchase earthquake insurance. Both decision trees and random forests demonstrated good predictive capabilities for identifying earthquake insurance uptake. Notably, random forests exhibited higher precision and robustness, emerging as an encouraging choice for earthquake insurance modeling and other classification problems. Empirically, we highlight the importance of insurance as an environmental risk management tool and emphasize the need for awareness and education on earthquake insurance as well as the use of supervised machine learning algorithms for classification problems.
Stroma-associated FSTL3 is a factor of calcium channel-derived tumor fibrosis
Authors: Li Jie-pin , Liu Yuan-jie , Yin Yi , Li Ruo-nan , Huang Wei , Zou Xi 
Published: December 2023

AbstractHepatocellular carcinoma (HCC) is the most widespread histological form of primary liver cancer, and it faces great diagnostic and therapeutic difficulties owing to its tumor diversity. Herein, we aim to establish a unique prognostic molecular subtype (MST) and based on this to find potential therapeutic targets to develop new immunotherapeutic strategies. Using calcium channel molecules expression-based consensus clustering, we screened 371 HCC patients from The Cancer Genome Atlas to screen for possible MSTs. We distinguished core differential gene modules between varying MSTs, and Tumor Immune Dysfunction and Exclusion scores were employed for the reliable assessment of HCC patient immunotherapeutic response rate. Immunohistochemistry and Immunofluorescence staining were used for validation of predicted immunotherapy outcomes and underlying biological mechanisms, respectively. We identified two MSTs with different clinical characteristics and prognoses. Based on the significant differences between the two MSTs, we further identified Follistatin-like 3 (FSTL3) as a potential indicator of immunotherapy resistance and validated this result in our own cohort. Finally, we found that FSTL3 is predominantly expressed in HCC stromal components and that it is a factor in enhancing fibroblast-M2 macrophage signaling crosstalk, the function of which is relevant to the pathogenesis of HCC. The presence of two MSTs associated with the calcium channel phenotype in HCC patients may provide promising directions for overcoming immunotherapy resistance in HCC, and the promotion of FSTL3 expressed in stromal components for HCC hyperfibrosis may be responsible for the poor response rate to immunotherapy in Cluster 2 (C2) patients.