bluuebunny/crossref_metadata_2025_split huggingface dataset:
Constraining the trend in the ocean CO2 sink during 2000–2022
Authors: Mayot Nicolas , Buitenhuis Erik T. , Wright Rebecca M. , Hauck Judith , Bakker Dorothee C. E. , Le Quéré Corinne 
Published: September 2024

AbstractThe ocean will ultimately store most of the CO2 emitted to the atmosphere by human activities. Despite its importance, estimates of the 2000−2022 trend in the ocean CO2 sink differ by a factor of two between observation-based products and process-based models. Here we address this discrepancy using a hybrid approach that preserves the consistency of known processes but constrains the outcome using observations. We show that the hybrid approach reproduces the stagnation of the ocean CO2 sink in the 1990s and its reinvigoration in the 2000s suggested by observation-based products and matches their amplitude. It suggests that process-based models underestimate the amplitude of the decadal variability in the ocean CO2 sink, but that observation-based products on average overestimate the decadal trend in the 2010s. The hybrid approach constrains the 2000−2022 trend in the ocean CO2 sink to 0.42 ± 0.06 Pg C yr−1 decade−1, and by inference the total land CO2 sink to 0.28 ± 0.13 Pg C yr−1 decade−1.
Plastic pollution in agricultural landscapes: an overlooked threat to pollination, biocontrol and food security
Authors: Sheng Dong , Jing Siyuan , He Xueqing , Klein Alexandra-Maria , Köhler Heinz-R. , Wanger Thomas C. 
Published: September 2024

AbstractEcosystem services such as pollination and biocontrol may be severely affected by emerging nano/micro-plastics (NMP) pollution. Here, we synthesize the little-known effects of NMP on pollinators and biocontrol agents on the organismal, farm and landscape scale. Ingested NMP trigger organismal changes from gene expression, organ damage to behavior modifications. At the farm and landscape level, NMP will likely amplify synergistic effects with other threats such as pathogens, and may alter floral resource distributions in high NMP concentration areas. Understanding exposure pathways of NMP on pollinators and biocontrol agents is critical to evaluate future risks for agricultural ecosystems and food security.
Cytolysin A is an intracellularly induced and secreted cytotoxin of typhoidal Salmonella
Authors: Krone Lena , Mahankali Srujita , Geiger Tobias 
Published: September 2024

AbstractTyphoidal Salmonella enterica serovars, such as Typhi and Paratyphi A, cause severe systemic infections, thereby posing a significant threat as human-adapted pathogens. This study focuses on cytolysin A (ClyA), a virulence factor essential for bacterial dissemination within the human body. We show that ClyA is exclusively expressed by intracellular S. Paratyphi A within the Salmonella-containing vacuole (SCV), regulated by the PhoP/Q system and SlyA. ClyA localizes in the bacterial periplasm, suggesting potential secretion. Deletion of TtsA, an essential Type 10 Secretion System component, completely abolishes intracellular ClyA detection and its presence in host cell supernatants. Host cells infected with wild-type S. Paratyphi A contain substantial ClyA, with supernatants capable of lysing neighboring cells. Notably, ClyA selectively lyses macrophages and erythrocytes while sparing epithelial cells. These findings identify ClyA as an intracellularly induced cytolysin, dependent on the SCV environment and secreted via a Type 10 Secretion System, with specific cytolytic activity.
Nitrogen accountancy in space agriculture
Authors: Yates Kevin , Berliner Aaron J. , Makrygiorgos Georgios , Kaiyom Farrah , McNulty Matthew J. , Khan Imran , Kusuma Paul , Kinlaw Claire , Miron Diogo , Legg Charles , Wilson James , Bugbee Bruce , Mesbah Ali , Arkin Adam P. , Nandi Somen , McDonald Karen A. 
Published: September 2024

AbstractFood production and pharmaceutical synthesis are posited as essential biotechnologies for facilitating human exploration beyond Earth. These technologies not only offer critical green space and food agency to astronauts but also promise to minimize mass and volume requirements through scalable, modular agriculture within closed-loop systems, offering an advantage over traditional bring-along strategies. Despite these benefits, the prevalent model for evaluating such systems exhibits significant limitations. It lacks comprehensive inventory and mass balance analyses for crop cultivation and life support, and fails to consider the complexities introduced by cultivating multiple crop varieties, which is crucial for enhancing food diversity and nutritional value. Here we expand space agriculture modeling to account for nitrogen dependence across an array of crops and demonstrate our model with experimental fitting of parameters. By adding nitrogen limitations, an extended model can account for potential interruptions in feedstock supply. Furthermore, sensitivity analysis was used to distill key consequential parameters that may be the focus of future experimental efforts.
Protocol for certifying entanglement in surface spin systems using a scanning tunneling microscope
Authors: Broekhoven Rik , Lee Curie , Phark Soo-hyon , Otte Sander , Wolf Christoph 
Published: September 2024

AbstractCertifying quantum entanglement is a critical step toward realizing quantum-coherent applications. In this work, we show that entanglement of spins can be unambiguously evidenced in a scanning tunneling microscope with electron spin resonance by exploiting the fact that entangled states undergo a free time evolution with a distinct characteristic time constant that clearly distinguishes it from the time evolution of non-entangled states. By implementing a phase control scheme, the phase of this time evolution can be mapped back onto the population of one entangled spin, which can then be read out reliably using a weakly coupled sensor spin in the junction of the scanning tunneling microscope. We demonstrate through open quantum system simulations with currently available spin coherence times of T2 ≈ 300 ns, that a signal directly correlated with the degree of entanglement can be measured at temperatures of 100–400 mK accessible in sub-Kelvin scanning tunneling microscopes.
1.5 million materials narratives generated by chatbots
Authors: Park Yang Jeong , Jerng Sung Eun , Yoon Sungroh , Li Ju 
Published: September 2024

AbstractThe advent of artificial intelligence (AI) has enabled a comprehensive exploration of materials for various applications. However, AI models often prioritize frequently encountered material examples in the scientific literature, limiting the selection of suitable candidates based on inherent physical and chemical attributes. To address this imbalance, we generated a dataset consisting of 1,453,493 natural language-material narratives from OQMD, Materials Project, JARVIS, and AFLOW2 databases based on ab initio calculation results that are more evenly distributed across the periodic table. The generated text narratives were then scored by both human experts and GPT-4, based on three rubrics: technical accuracy, language and structure, and relevance and depth of content, showing similar scores but with human-scored depth of content being the most lagging. The integration of multimodal data sources and large language models holds immense potential for AI frameworks to aid the exploration and discovery of solid-state materials for specific applications of interest.
Exploring the efficacy of memory specificity training on depression among Iranian adolescents: a comparative analysis of online vs. in-person delivery
Authors: Salamat Mohsen , Moradi Alireza , Hasani Jafar , Farahimanesh Sharareh , Ayatmehr Fateme , Yavarzadeh Hanieh , Jobson Laura 
Published: September 2024

AbstractDepression in adolescence is common worldwide, with the burden being highest in low- and middle-income countries. This study assessed the efficacy of in-person Memory Specificity Training (MeST) and computerized MeST (c-MeST) as cognitive training programs aimed at addressing depression among Iranian adolescents. A secondary aim was to evaluate the efficacy of MeST and c-MeST on autobiographical memory specificity, emotion regulation and cognitive control. Ninety Iranian male adolescents (aged 13–18 years) with depression were randomly assigned to three groups; MeST group (n = 30), c-MeST group (n = 30) and the non-active control group (n = 30). Participants completed the Beck Depression Inventory-II, Autobiographical Memory Test, Cognitive Emotion Regulation Questionnaire, Wisconsin Card Sorting Test and Stroop Color and Word Test. The groups underwent either MeST and c-MeST. All the assessments were re-conducted after the intervention (post-intervention) and at 1-month post-intervention (follow-up). The in-person MeST group exhibited significantly higher autobiographical memory specificity at post-intervention and follow-up compared to the c-MeST group. Both groups demonstrated significantly lower levels of depression at post-intervention and follow-up. Both groups showed improvements in emotion regulation and cognitive control, which were found to mediate improvements in depression symptomatology. c-MeST and MeST appear promising brief interventions for the treatment of depression among adolescents in Iran.
Forecasting national CO2 emissions worldwide
Authors: Costantini Lorenzo , Laio Francesco , Mariani Manuel Sebastian , Ridolfi Luca , Sciarra Carla 
Published: September 2024

AbstractUrgent climate action, especially carbon emissions reduction, is required to achieve sustainable goals. Therefore, understanding the drivers of and predicting $$\hbox {CO}_2$$ emissions is a compelling matter. We present two global modeling frameworks—a multivariate regression and a Random Forest Regressor (RFR)—to hindcast (until 2021) and forecast (up to 2035) $$\hbox {CO}_2$$ emissions across 117 countries as driven by 12 socioeconomic indicators regarding carbon emissions, economic well-being, green and complexity economics, energy use and consumption. Our results identify key driving features to explain emissions pathways, where beyond-GDP indicators rooted in the Economic Complexity field emerge. Considering current countries’ development status, divergent emission dynamics appear. According to the RFR, a −6.2% reduction is predicted for developed economies by 2035 and a +19% increase for developing ones (referring to 2020), thus stressing the need to promote green growth and sustainable development in low-capacity contexts.
Chemical characterization of hibiscus rosa-sinensis plant fibers facilitated through design of experiments and artificial neural network hybrid approach
Authors: Supriya J. P. , Shetty Raviraj , Naik Nithesh , Maddasani Srinivasulu , Hegde Adithya 
Published: September 2024

AbstractThe integration of natural fibers into Fiber Reinforced Polymers (FRPs) has emerged as a promising avenue for sustainable and high-performance composite materials. Natural fibers, derived from plants, offer notable advantages such as renewability, low cost, and environmental friendliness. Among these natural fibers, Hibiscus Rosa-Sinensis (HRS) plant fibers have gained significant attention owing to their widespread availability and unique mechanical properties. In this study, HRS fibers were chemically treated using Sodium Hydroxide (NaOH), Potassium Permanganate (KMnO4), and Acetic Acid (CH3COOH) at different weight percentages (3, 4, 5 Wt.%) and solutionizing times (1, 2, 3 h) based on Taguchi’s L27 orthogonal array. The fibers, extracted from epidermis of the stems, underwent cleaning and chemical treatment after water retting. The crystallinity index, determined via X-ray Diffraction (XRD), indicated a maximum value of 65.77%. Thermo-gravimetric analysis (TGA) exhibited a degradation temperature of 365.24 °C and a material loss of 63.11%. Potassium Permanganate treatment at 4 Wt.% and 3 h of solutionizing time has yielded the best results. Multi-Layer Perceptron Artificial Neural Network (MLP-ANN) has been successfully applied to accurately predict the output physical characteristics of chemically treated HRS fibers using experimental data. The results are in close alignment with the literature. Scanning Electron Microscopy (SEM) and Energy Dispersive X-ray Spectroscopy (EDS) analyses have provided valuable insights into the microstructure and constituents of the chemically treated HRS fibers. This research emphasises on the effectiveness of the chemical treatment process in enhancing the properties of HRS plant fibers for potential composite applications.
Determination of key hub genes in Leishmaniasis as potential factors in diagnosis and treatment based on a bioinformatics study
Authors: Safaei Mohsen , Goodarzi Arash , Abpeikar Zahra , Farmani Ahmad Reza , Kouhpayeh Seyed Amin , Najafipour Sohrab , Jafari Najaf Abadi Mohammad Hassan 
Published: September 2024

AbstractLeishmaniasis is an infectious disease caused by protozoan parasites from different species of leishmania. The disease is transmitted by female sandflies that carry these parasites. In this study, datasets on leishmaniasis published in the GEO database were analyzed and summarized. The analysis in all three datasets (GSE43880, GSE55664, and GSE63931) used in this study has been performed on the skin wounds of patients infected with a clinical form of leishmania (Leishmania braziliensis), and biopsies have been taken from them. To identify differentially expressed genes (DEGs) between leishmaniasis patients and controls, the robust rank aggregation (RRA) procedure was applied. We performed gene functional annotation and protein-protein interaction (PPI) network analysis to demonstrate the putative functionalities of the DEGs. The study utilized Molecular Complex Detection (MCODE), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) to detect molecular complexes within the protein-protein interaction (PPI) network and conduct analyses on the identified functional modules. The CytoHubba plugin’s results were paired with RRA analysis to determine the hub genes. Finally, the interaction between miRNAs and hub genes was predicted. Based on the RRA integrated analysis, 407 DEGs were identified (263 up-regulated genes and 144 down-regulated genes). The top three modules were listed after creating the PPI network via the MCODE plug. Seven hub genes were found using the CytoHubba app and RRA: CXCL10, GBP1, GNLY, GZMA, GZMB, NKG7, and UBD. According to our enrichment analysis, these functional modules were primarily associated with immune pathways, cytokine activity/signaling pathways, and inflammation pathways. However, a UBD hub gene is interestingly involved in the ubiquitination pathways of pathogenesis. The mirNet database predicted the hub gene’s interaction with miRNAs, and results revealed that several miRNAs, including mir-146a-5p, crucial in fighting pathogenesis. The key hub genes discovered in this work may be considered as potential biomarkers in diagnosis, development of agonists/antagonist, novel vaccine design, and will greatly contribute to clinical studies in the future.
Enhanced gastric cancer classification and quantification interpretable framework using digital histopathology images
Authors: Zubair Muhammad , Owais Muhammad , Mahmood Tahir , Iqbal Saeed , Usman Syed Muhammad , Hussain Irfan 
Published: September 2024

AbstractRecent developments have highlighted the critical role that computer-aided diagnosis (CAD) systems play in analyzing whole-slide digital histopathology images for detecting gastric cancer (GC). We present a novel framework for gastric histology classification and segmentation (GHCS) that offers modest yet meaningful improvements over existing CAD models for GC classification and segmentation. Our methodology achieves marginal improvements over conventional deep learning (DL) and machine learning (ML) models by adaptively focusing on pertinent characteristics of images. This contributes significantly to our study, highlighting that the proposed model, which performs well on normalized images, is robust in certain respects, particularly in handling variability and generalizing to different datasets. We anticipate that this robustness will lead to better results across various datasets. An expectation-maximizing Naïve Bayes classifier that uses an updated Gaussian Mixture Model is at the heart of the suggested GHCS framework. The effectiveness of our classifier is demonstrated by experimental validation on two publicly available datasets, which produced exceptional classification accuracies of 98.87% and 97.28% on validation sets and 98.47% and 97.31% on test sets. Our framework shows a slight but consistent improvement over previously existing techniques in gastric histopathology image classification tasks, as demonstrated by comparative analysis. This may be attributed to its ability to capture critical features of gastric histopathology images better. Furthermore, using an improved Fuzzy c-means method, our study produces good results in GC histopathology picture segmentation, outperforming state-of-the-art segmentation models with a Dice coefficient of 65.21% and a Jaccard index of 60.24%. The model’s interpretability is complemented by Grad-CAM visualizations, which help understand the decision-making process and increase the model’s trustworthiness for end-users, especially clinicians.
Variability of marine heatwaves’ characteristics and assessment of their potential drivers in the Baltic Sea over the last 42 years
Authors: Bashiri Behzad , Barzandeh Amirhossein , Männik Aarne , Raudsepp Urmas 
Published: September 2024

Abstract
This study examined Baltic Sea Marine Heatwaves (MHWs) using 42 years of satellite-derived sea surface temperature (SST) data. We found that MHWs in warmer months are more intense but shorter compared to MHWs in cooler months. Also, MHWs predominantly affect offshore areas in warmer months, whereas MHWs predominantly impacting coastal seas in cooler months, especially along the eastern coast. Our analysis of interannual variability revealed that, unlike in many other basins worldwide, Baltic MHWs tend to maintain a constant intensity, while their spatial extent has significantly increased over the last few decades. Shortwave radiation notably influences MHW intensity and spatial extent, with additional impacts from longwave radiation in cooler months and latent heat flux in warmer months. Northern Hemisphere teleconnections exhibit stronger correlations with MHWs in the Baltic Sea compared to global-scale climate oscillations, with the Eastern Atlantic pattern having a particularly significant effect on MHW variability in the region.
Evolution of a biological thermocouple by adaptation of cytochrome c oxidase in a subterrestrial metazoan, Halicephalobus mephisto
Authors: Guerin Megan N. , Ellis TreVaughn S. , Ware Mark J. , Manning Alexandra , Coley Ariana A. , Amini Ali , Igboanugo Adaeze G. , Rothrock Amaya P. , Chung George , Gunsalus Kristin C. , Bracht John R. 
Published: September 2024

AbstractIn this study, we report a biological temperature-sensing electrical regulator in the cytochrome c oxidase of the Devil Worm, Halicephalobus mephisto. This extremophile metazoan was isolated 1.3 km underground in a South African goldmine, where it adapted to heat and potentially to hypoxia, making its mitochondrial sequence a likely target of adaptational change. We obtained the complete mitochondrial genome sequence of this organism and show through dN/dS analysis evidence of positive selection in H. mephisto cytochrome c oxidase subunits. Seventeen of these positively selected amino acid substitutions were located in proximity to the H- and K-pathway proton channels of the complex. Surprisingly, the H. mephisto cytochrome c oxidase completely shuts down at low temperatures (20 °C), leading to a 4.8-fold reduction in the transmembrane proton gradient (ΔΨm) compared to optimal temperature (37 °C). Direct measurement of oxygen consumption found a corresponding 4.6-fold drop at 20 °C compared to 37 °C. Correspondingly, the lifecycle of H. mephisto takes four times longer at low temperature than at higher. This elegant evolutionary adaptation creates a finely-tuned mitochondrial temperature sensor, allowing this ectothermic organism to maximize its reproductive success across varying environmental temperatures.
Imaging genetics of language network functional connectivity reveals links with language-related abilities, dyslexia and handedness
Authors: Amelink Jitse S. , Postema Merel C. , Kong Xiang-Zhen , Schijven Dick , Carrión-Castillo Amaia , Soheili-Nezhad Sourena , Sha Zhiqiang , Molz Barbara , Joliot Marc , Fisher Simon E. , Francks Clyde 
Published: September 2024

AbstractLanguage is supported by a distributed network of brain regions with a particular contribution from the left hemisphere. A multi-level understanding of this network requires studying its genetic architecture. We used resting-state imaging data from 29,681 participants (UK Biobank) to measure connectivity between 18 left-hemisphere regions involved in multimodal sentence-level processing, as well as their right-hemisphere homotopes, and interhemispheric connections. Multivariate genome-wide association analysis of this total network, based on genetic variants with population frequencies >1%, identified 14 genomic loci, of which three were also associated with asymmetry of intrahemispheric connectivity. Polygenic dispositions to lower language-related abilities, dyslexia and left-handedness were associated with generally reduced leftward asymmetry of functional connectivity. Exome-wide association analysis based on rare, protein-altering variants (frequencies <1%) suggested 7 additional genes. These findings shed new light on genetic contributions to language network organization and related behavioural traits.
Cyclopamine modulates smoothened receptor activity in a binding position dependent manner
Authors: Kim Kihong , Bansal Prateek D. , Shukla Diwakar 
Published: September 2024

AbstractCyclopamine, a natural alkaloid, can act as an agonist when it binds to the Cysteine-Rich Domain (CRD) of Smoothened receptor and as an antagonist when it binds to the Transmembrane Domain (TMD). To study the effect of cyclopamine binding to each site experimentally, mutations in the other site are required. Hence, simulations are critical for understanding the WT activity due to binding at different sites. Using multi-milliseconds long aggregate MD simulations combined with Markov state models and machine learning, we explore the dynamic behavior of cyclopamine’s interactions with different domains of WT SMO. A higher population of the active state at equilibrium, a lower free energy barrier of ~2 kcal/mol, and expansion of hydrophobic tunnel to facilitate cholesterol transport agrees with cyclopamine’s agonistic behavior when bound to CRD. A higher population of the inactive state at equilibrium, a higher free energy barrier of ~4 kcal/mol and restricted hydrophobic tunnel shows cyclopamine’s antagonistic behavior when bound to TMD. With cyclopamine bound to both sites, there is a slightly larger inactive population at equilibrium and an increased free energy barrier (~3.5 kcal/mol) exhibiting an overall weak antagonistic effect. These findings show cyclopamine’s domain-specific modulation of SMO regulates Hedgehog signaling and cholesterol transport.