bluuebunny/crossref_metadata_2025_split huggingface dataset:
BilR is a gut microbial enzyme that reduces bilirubin to urobilinogen
Authors: Hall Brantley , Levy Sophia , Dufault-Thompson Keith , Arp Gabriela , Zhong Aoshu , Ndjite Glory Minabou , Weiss Ashley , Braccia Domenick , Jenkins Conor , Grant Maggie R. , Abeysinghe Stephenie , Yang Yiyan , Jermain Madison D. , Wu Chih Hao , Ma Bing , Jiang Xiaofang 
Published: January 2024

AbstractMetabolism of haem by-products such as bilirubin by humans and their gut microbiota is essential to human health, as excess serum bilirubin can cause jaundice and even neurological damage. The bacterial enzymes that reduce bilirubin to urobilinogen, a key step in this pathway, have remained unidentified. Here we used biochemical analyses and comparative genomics to identify BilR as a gut-microbiota-derived bilirubin reductase that reduces bilirubin to urobilinogen. We delineated the BilR sequences from similar reductases through the identification of key residues critical for bilirubin reduction and found that BilR is predominantly encoded by Firmicutes species. Analysis of human gut metagenomes revealed that BilR is nearly ubiquitous in healthy adults, but prevalence is decreased in neonates and individuals with inflammatory bowel disease. This discovery sheds light on the role of the gut microbiome in bilirubin metabolism and highlights the significance of the gut–liver axis in maintaining bilirubin homeostasis.
Heating and cooling are fundamentally asymmetric and evolve along distinct pathways
Authors: Ibáñez M. , Dieball C. , Lasanta A. , Godec A. , Rica R. A. 
Published: January 2024

AbstractAccording to conventional wisdom, a system placed in an environment with a different temperature tends to relax to the temperature of the latter, mediated by the flows of heat or matter that are set solely by the temperature difference. It is becoming clear, however, that thermal relaxation is much more intricate when temperature changes push the system far from thermodynamic equilibrium. Here, by using an optically trapped colloidal particle, we show that microscale systems under such conditions heat up faster than they cool down. We find that between any pair of temperatures, heating is not only faster than cooling but the respective processes, in fact, evolve along fundamentally distinct pathways, which we explain with a new theoretical framework that we call thermal kinematics. Our results change the view of thermalization at the microscale and will have a strong impact on energy-conversion applications and thermal management of microscopic devices, particularly in the operation of Brownian heat engines.
Disordered enthalpy–entropy descriptor for high-entropy ceramics discovery
Authors: Divilov Simon , Eckert Hagen , Hicks David , Oses Corey , Toher Cormac , Friedrich Rico , Esters Marco , Mehl Michael J. , Zettel Adam C. , Lederer Yoav , Zurek Eva , Maria Jon-Paul , Brenner Donald W. , Campilongo Xiomara , Filipović Suzana , Fahrenholtz William G. , Ryan Caillin J. , DeSalle Christopher M. , Crealese Ryan J. , Wolfe Douglas E. , Calzolari Arrigo , Curtarolo Stefano 
Published: January 2024

AbstractThe need for improved functionalities in extreme environments is fuelling interest in high-entropy ceramics1–3. Except for the computational discovery of high-entropy carbides, performed with the entropy-forming-ability descriptor4, most innovation has been slowly driven by experimental means1–3. Hence, advancement in the field needs more theoretical contributions. Here we introduce disordered enthalpy–entropy descriptor (DEED), a descriptor that captures the balance between entropy gains and enthalpy costs, allowing the correct classification of functional synthesizability of multicomponent ceramics, regardless of chemistry and structure. To make our calculations possible, we have developed a convolutional algorithm that drastically reduces computational resources. Moreover, DEED guides the experimental discovery of new single-phase high-entropy carbonitrides and borides. This work, integrated into the AFLOW computational ecosystem, provides an array of potential new candidates, ripe for experimental discoveries.
Nav1.7 as a chondrocyte regulator and therapeutic target for osteoarthritis
Authors: Fu Wenyu , Vasylyev Dmytro , Bi Yufei , Zhang Mingshuang , Sun Guodong , Khleborodova Asya , Huang Guiwu , Zhao Libo , Zhou Renpeng , Li Yonggang , Liu Shujun , Cai Xianyi , He Wenjun , Cui Min , Zhao Xiangli , Hettinghouse Aubryanna , Good Julia , Kim Ellen , Strauss Eric , Leucht Philipp , Schwarzkopf Ran , Guo Edward X. , Samuels Jonathan , Hu Wenhuo , Attur Mukundan , Waxman Stephen G. , Liu Chuan-ju 
Published: January 2024

AbstractOsteoarthritis (OA) is the most common joint disease. Currently there are no effective methods that simultaneously prevent joint degeneration and reduce pain1. Although limited evidence suggests the existence of voltage-gated sodium channels (VGSCs) in chondrocytes2, their expression and function in chondrocytes and in OA remain essentially unknown. Here we identify Nav1.7 as an OA-associated VGSC and demonstrate that human OA chondrocytes express functional Nav1.7 channels, with a density of 0.1 to 0.15 channels per µm2 and 350 to 525 channels per cell. Serial genetic ablation of Nav1.7 in multiple mouse models demonstrates that Nav1.7 expressed in dorsal root ganglia neurons is involved in pain, whereas Nav1.7 in chondrocytes regulates OA progression. Pharmacological blockade of Nav1.7 with selective or clinically used pan-Nav channel blockers significantly ameliorates the progression of structural joint damage, and reduces OA pain behaviour. Mechanistically, Nav1.7 blockers regulate intracellular Ca2+ signalling and the chondrocyte secretome, which in turn affects chondrocyte biology and OA progression. Identification of Nav1.7 as a novel chondrocyte-expressed, OA-associated channel uncovers a dual target for the development of disease-modifying and non-opioid pain relief treatment for OA.
The logic of recurrent circuits in the primary visual cortex
Authors: Oldenburg Ian Antón , Hendricks William D. , Handy Gregory , Shamardani Kiarash , Bounds Hayley A. , Doiron Brent , Adesnik Hillel 
Published: January 2024

AbstractRecurrent cortical activity sculpts visual perception by refining, amplifying or suppressing visual input. However, the rules that govern the influence of recurrent activity remain enigmatic. We used ensemble-specific two-photon optogenetics in the mouse visual cortex to isolate the impact of recurrent activity from external visual input. We found that the spatial arrangement and the visual feature preference of the stimulated ensemble and the neighboring neurons jointly determine the net effect of recurrent activity. Photoactivation of these ensembles drives suppression in all cells beyond 30 µm but uniformly drives activation in closer similarly tuned cells. In nonsimilarly tuned cells, compact, cotuned ensembles drive net suppression, while diffuse, cotuned ensembles drive activation. Computational modeling suggests that highly local recurrent excitatory connectivity and selective convergence onto inhibitory neurons explain these effects. Our findings reveal a straightforward logic in which space and feature preference of cortical ensembles determine their impact on local recurrent activity.
Differences in the nutritional quality of improved finger millet genotypes in Ethiopia
Authors: Teklu Demeke , Gashu Dawd , Joy Edward J. M. , Bailey Elizabeth H. , Wilson Lolita , Amede Tilahun , Broadley Martin R. 
Published: January 2024

AbstractImproved crop genotypes are constantly introduced. However, information on their nutritional quality is generally limited. The present study reports the proximate composition and the concentration and relative bioavailability of minerals of improved finger millets of different genotypes. Grains of finger millet genotypes (n = 15) grown in research station during 2019 and 2020 in Ethiopia, and replicated three times in a randomized complete block design, were analysed for proximate composition, mineral concentration (iron, zinc, calcium, selenium), and antinutritional factors (phytate, tannin and oxalate). Moreover, the antinutritional factors to mineral molar ratio method was used to estimate mineral bioavailability. The result shows a significant genotypic variation in protein, fat and fibre level, ranging from 10% to 14.6%, 1.0 to 3.8%, and 1.4 to 4.6%, respectively. Similarly, different finger millets genotypes had significantly different mineral concentrations ranging from 3762 ± 332 to 5893 ± 353 mg kg−1 for Ca, 19.9 ± 1.6 to 26.2 ± 2.7 mg kg−1 for Zn, 36.3 ± 4.6 to 52.9 ± 9.1 mg kg−1 for Fe and 36.6 ± 11 to 60.9 ± 22 µg kg−1 for Se. Phytate (308–360 µg g−1), tannin (0.15–0.51 mg g−1) and oxalate (1.26–4.41 mg g−1) concentrations were also influenced by genotype. Antinutritional factors to minerals molar ratio were also significantly different by genotypes but were below the threshold for low mineral bioavailability. Genotype significantly influenced mineral and antinutritional concentrations of finger millet grains. In addition, all finger millet genotypes possess good mineral bioavailability. Especially, the high Ca concentration in finger millet, compared to in other cereals, could play a vital role to combating Ca deficiency. The result suggests the different finger millet genotypes possess good nutrient content and may contribute to the nutrition security of the local people.
Predicting cell types with supervised contrastive learning on cells and their types
Authors: Heryanto Yusri Dwi , Zhang Yao-zhong , Imoto Seiya 
Published: January 2024

AbstractSingle-cell RNA-sequencing (scRNA-seq) is a powerful technique that provides high-resolution expression profiling of individual cells. It significantly advances our understanding of cellular diversity and function. Despite its potential, the analysis of scRNA-seq data poses considerable challenges related to multicollinearity, data imbalance, and batch effect. One of the pivotal tasks in single-cell data analysis is cell type annotation, which classifies cells into discrete types based on their gene expression profiles. In this work, we propose a novel modeling formalism for cell type annotation with a supervised contrastive learning method, named SCLSC (Supervised Contrastive Learning for Single Cell). Different from the previous usage of contrastive learning in single cell data analysis, we employed the contrastive learning for instance-type pairs instead of instance-instance pairs. More specifically, in the cell type annotation task, the contrastive learning is applied to learn cell and cell type representation that render cells of the same type to be clustered in the new embedding space. Through this approach, the knowledge derived from annotated cells is transferred to the feature representation for scRNA-seq data. The whole training process becomes more efficient when conducting contrastive learning for cell and their types. Our experiment results demonstrate that the proposed SCLSC method consistently achieves superior accuracy in predicting cell types compared to five state-of-the-art methods. SCLSC also performs well in identifying cell types in different batch groups. The simplicity of our method allows for scalability, making it suitable for analyzing datasets with a large number of cells. In a real-world application of SCLSC to monitor the dynamics of immune cell subpopulations over time, SCLSC demonstrates a capability to discriminate cell subtypes of CD19+ B cells that were not present in the training dataset.
Single-cell sequencing dissects the transcriptional identity of activated fibroblasts and identifies novel persistent distal tubular injury patterns in kidney fibrosis
Authors: Rudman-Melnick Valeria , Adam Mike , Stowers Kaitlynn , Potter Andrew , Ma Qing , Chokshi Saagar M. , Vanhoutte Davy , Valiente-Alandi Iñigo , Lindquist Diana M. , Nieman Michelle L. , Kofron J. Matthew , Chung Eunah , Park Joo-Seop , Potter S. Steven , Devarajan Prasad 
Published: January 2024

AbstractExamining kidney fibrosis is crucial for mechanistic understanding and developing targeted strategies against chronic kidney disease (CKD). Persistent fibroblast activation and tubular epithelial cell (TEC) injury are key CKD contributors. However, cellular and transcriptional landscapes of CKD and specific activated kidney fibroblast clusters remain elusive. Here, we analyzed single cell transcriptomic profiles of two clinically relevant kidney fibrosis models which induced robust kidney parenchymal remodeling. We dissected the molecular and cellular landscapes of kidney stroma and newly identified three distinctive fibroblast clusters with “secretory”, “contractile” and “vascular” transcriptional enrichments. Also, both injuries generated failed repair TECs (frTECs) characterized by decline of mature epithelial markers and elevation of stromal and injury markers. Notably, frTECs shared transcriptional identity with distal nephron segments of the embryonic kidney. Moreover, we identified that both models exhibited robust and previously unrecognized distal spatial pattern of TEC injury, outlined by persistent elevation of renal TEC injury markers including Krt8 and Vcam1, while the surviving proximal tubules (PTs) showed restored transcriptional signature. We also found that long-term kidney injuries activated a prominent nephrogenic signature, including Sox4 and Hox gene elevation, which prevailed in the distal tubular segments. Our findings might advance understanding of and targeted intervention in fibrotic kidney disease.
Molecular detection of Citrus exocortis viroid (CEVd), Citrus viroid-III (CVd-III), and Citrus viroid-IV (CVd-IV) in Palestine
Authors: Abualrob Aswar , Alabdallah Osama , Kubaa Raied Abou , Naser Sabri M. , Alkowni Raed 
Published: January 2024

AbstractCitrus hosts various phytopathogens that have impacted productivity, including viroids. Missing data on the status of viroids in citrus in Palestine were not reported. This study was aimed to detect any of Citrus exocortis viroid (CEVd), Citrus viroid-III (CVd-III), and Citrus viroid-IV (CVd-IV) in the Palestinian National Agricultural Research Center (NARC) germplasm collection Field inspections found symptoms such as leaf epinasty; vein discoloration, and bark cracking on various citrus varieties. RT-PCR revealed a significant prevalence of CVd-IV; CEVd and CVd-III (47%, 31%, and 22%; respectively). CVd-III variants with 91.3% nucleic acid sequence homology have been reported. The sequence of each viroid were deposited in GenBank as (OP925746 for CEVd, OP902248 and OP902249 for CVd-III-PS-1 and -PS-2 isolates, and OP902247 for CVd-IV). This was the first to report three of citrus viroids in Palestine, appealing to apply of phytosanitary measures to disseminate healthy propagating materials free from viroids.
Deficiency of the lipid flippase ATP10A causes diet-induced dyslipidemia in female mice
Authors: Norris Adriana C. , Yazlovitskaya Eugenia M. , Zhu Lin , Rose Bailey S. , May Jody C. , Gibson-Corley Katherine N. , McLean John A. , Stafford John M. , Graham Todd R. 
Published: January 2024

AbstractGenetic association studies have linked ATP10A and closely related type IV P-type ATPases (P4-ATPases) to insulin resistance and vascular complications, such as atherosclerosis. ATP10A translocates phosphatidylcholine and glucosylceramide across cell membranes, and these lipids or their metabolites play important roles in signal transduction pathways regulating metabolism. However, the influence of ATP10A on lipid metabolism in mice has not been explored. Here, we generated gene-specific Atp10A knockout mice and show that Atp10A−/− mice fed a high-fat diet did not gain excess weight relative to wild-type littermates. However, Atp10A−/− mice displayed female-specific dyslipidemia characterized by elevated plasma triglycerides, free fatty acids and cholesterol, as well as altered VLDL and HDL properties. We also observed increased circulating levels of several sphingolipid species along with reduced levels of eicosanoids and bile acids. The Atp10A−/− mice also displayed hepatic insulin resistance without perturbations to whole-body glucose homeostasis. Thus, ATP10A has a sex-specific role in regulating plasma lipid composition and maintaining hepatic liver insulin sensitivity in mice.
Role of the mechanical microenvironment on CD-44 expression of breast adenocarcinoma in response to radiotherapy
Authors: Frascogna Crescenzo , Mottareale Rocco , La Verde Giuseppe , Arrichiello Cecilia , Muto Paolo , Netti Paolo A. , Pugliese Mariagabriella , Panzetta Valeria 
Published: January 2024

AbstractThe biological effects of ionizing radiation are exploited in the clinical practice of radiotherapy to destroy tumour cells while sparing the surrounding normal tissue. While most of the radiotherapy research focused on DNA damage and repair, recently a great attention is going to cells' interactions with the mechanical microenvironment of both malignant and healthy tissues after exposure. In fact, the stiffness of the extracellular matrix can modify cells' motility and spreading through the modulation of transmembrane proteins and surface receptors' expression, such as CD-44. CD-44 receptor has held much interest also in targeted-therapy due to its affinity with hyaluronic acid, which can be used to functionalize biodegradable nanoparticles loaded with chemotherapy drugs for targeted therapy. We evaluated changes in CD-44 expression in two mammary carcinoma cell lines (MCF10A and MDA-MB-231) after exposure to X-ray (2 or 10 Gy). To explore the role of the mechanical microenvironment, we mimicked tissues' stiffness with polyacrylamide's substrates producing two different elastic modulus values (0.5 and 15 kPa). We measured a dose dependent increase in CD-44 relative expression in tumour cells cultured in a stiffer microenvironment. These findings highlight a crucial connection between the mechanical properties of the cell's surroundings and the post-radiotherapy expression of surface receptors.
Development and external validation of a clinical prediction model for predicting quality of recovery up to 1 week after surgery
Authors: van Beek Stefan , Nieboer Daan , Klimek Markus , Stolker Robert Jan , Mijderwijk Hendrik-Jan 
Published: January 2024

AbstractThe Quality of Recovery Score-40 (QoR-40) has been increasingly used for assessing recovery after patients undergoing surgery. However, a prediction model estimating quality of recovery is lacking. The aim of the present study was to develop and externally validate a clinical prediction model that predicts quality of recovery up to one week after surgery. The modelling procedure consisted of two models of increasing complexity (basic and full model). To assess the internal validity of the developed model, bootstrapping (1000 times) was applied. At external validation, the model performance was evaluated according to measures for overall model performance (explained variance (R2)) and calibration (calibration plot and slope). The full model consisted of age, sex, previous surgery, BMI, ASA classification, duration of surgery, HADS and preoperative QoR-40 score. At model development, the R2 of the full model was 0.24. At external validation the R2 dropped as expected. The calibration analysis showed that the QoR-40 predictions provided by the developed prediction models are reliable. The presented models can be used as a starting point for future updating in prediction studies. When the predictive performance is improved it could be implemented clinically in the future.
Electron-donating functional groups strengthen ligand-induced chiral imprinting on CsPbBr3 quantum dots
Authors: Dunlap-Shohl Wiley A. , Tabassum Nazifa , Zhang Peng , Shiby Elizabeth , Beratan David N. , Waldeck David H. 
Published: January 2024

AbstractChiral perovskite nanoparticles and films are promising for integration in emerging spintronic and optoelectronic technologies, yet few design rules exist to guide the development of chiral material properties. The chemical space of potential building blocks for these nanostructures is vast, and the mechanisms through which organic ligands can impart chirality to the inorganic perovskite lattice are not well understood. In this work, we investigate how the properties of chiral ammonium ligands, the most common organic ligand type used with perovskites, affect the circular dichroism of strongly quantum confined CsPbBr3 nanocrystals. We show that aromatic ammonium ligands with stronger electron-donating groups lead to higher-intensity circular dichroism associated with the lowest-energy excitonic transition of the perovskite nanocrystal. We argue that this behavior is best explained by a modulation of the exciton wavefunction overlap between the nanocrystal and the organic ligand, as the functional groups on the ligand can shift electron density toward the organic species-perovskite lattice interface to increase the imprinting.
EEG epilepsy seizure prediction: the post-processing stage as a chronology
Authors: Batista Joana , Pinto Mauro F. , Tavares Mariana , Lopes Fábio , Oliveira Ana , Teixeira César 
Published: January 2024

AbstractAlmost one-third of epileptic patients fail to achieve seizure control through anti-epileptic drug administration. In the scarcity of completely controlling a patient’s epilepsy, seizure prediction plays a significant role in clinical management and providing new therapeutic options such as warning or intervention devices. Seizure prediction algorithms aim to identify the preictal period that Electroencephalogram (EEG) signals can capture. However, this period is associated with substantial heterogeneity, varying among patients or even between seizures from the same patient. The present work proposes a patient-specific seizure prediction algorithm using post-processing techniques to explore the existence of a set of chronological events of brain activity that precedes epileptic seizures. The study was conducted with 37 patients with Temporal Lobe Epilepsy (TLE) from the EPILEPSIAE database. The designed methodology combines univariate linear features with a classifier based on Support Vector Machines (SVM) and two post-processing techniques to handle pre-seizure temporality in an easily explainable way, employing knowledge from network theory. In the Chronological Firing Power approach, we considered the preictal as a sequence of three brain activity events separated in time. In the Cumulative Firing Power approach, we assumed the preictal period as a sequence of three overlapping events. These methodologies were compared with a control approach based on the typical machine learning pipeline. We considered a Seizure Prediction horizon (SPH) of 5 mins and analyzed several values for the Seizure Occurrence Period (SOP) duration, between 10 and 55 mins. Our results showed that the Cumulative Firing Power approach may improve the seizure prediction performance. This new strategy performed above chance for 62% of patients, whereas the control approach only validated 49% of its models.