bluuebunny/crossref_metadata_2025_split huggingface dataset:
A commensal protozoan attenuates Clostridioides difficile pathogenesis in mice via arginine-ornithine metabolism and host intestinal immune response
Authors: Yang Huan , Wu Xiaoxiao , Li Xiao , Zang Wanqing , Zhou Zhou , Zhou Yuan , Cui Wenwen , Kou Yanbo , Wang Liang , Hu Ankang , Wu Lianlian , Yin Zhinan , Chen Quangang , Chen Ying , Huang Zhutao , Wang Yugang , Gu Bing 
Published: April 2024

AbstractAntibiotic-induced dysbiosis is a major risk factor for Clostridioides difficile infection (CDI), and fecal microbiota transplantation (FMT) is recommended for treating CDI. However, the underlying mechanisms remain unclear. Here, we show that Tritrichomonas musculis (T.mu), an integral member of the mouse gut commensal microbiota, reduces CDI-induced intestinal damage by inhibiting neutrophil recruitment and IL-1β secretion, while promoting Th1 cell differentiation and IFN-γ secretion, which in turn enhances goblet cell production and mucin secretion to protect the intestinal mucosa. T.mu can actively metabolize arginine, not only influencing the host’s arginine-ornithine metabolic pathway, but also shaping the metabolic environment for the microbial community in the host’s intestinal lumen. This leads to a relatively low ornithine state in the intestinal lumen in C. difficile-infected mice. These changes modulate C. difficile’s virulence and the host intestinal immune response, and thus collectively alleviating CDI. These findings strongly suggest interactions between an intestinal commensal eukaryote, a pathogenic bacterium, and the host immune system via inter-related arginine-ornithine metabolism in the regulation of pathogenesis and provide further insights for treating CDI.
In-depth correlation analysis between tear glucose and blood glucose using a wireless smart contact lens
Authors: Park Wonjung , Seo Hunkyu , Kim Jeongho , Hong Yeon-Mi , Song Hayoung , Joo Byung Jun , Kim Sumin , Kim Enji , Yae Che-Gyem , Kim Jeonghyun , Jin Jonghwa , Kim Joohee , Lee Yong-ho , Kim Jayoung , Kim Hong Kyun , Park Jang-Ung 
Published: April 2024

AbstractTears have emerged as a promising alternative to blood for diagnosing diabetes. Despite increasing attempts to measure tear glucose using smart contact lenses, the controversy surrounding the correlation between tear glucose and blood glucose still limits the clinical usage of tears. Herein, we present an in-depth investigation of the correlation between tear glucose and blood glucose using a wireless and soft smart contact lens for continuous monitoring of tear glucose. This smart contact lens is capable of quantitatively monitoring the tear glucose levels in basal tears excluding the effect of reflex tears which might weaken the relationship with blood glucose. Furthermore, this smart contact lens can provide an unprecedented level of continuous tear glucose data acquisition at sub-minute intervals. These advantages allow the precise estimation of lag time, enabling the establishment of the concept called ‘personalized lag time’. This demonstration considers individual differences and is successfully applied to both non-diabetic and diabetic humans, as well as in animal models, resulting in a high correlation.
Large area single crystal gold of single nanometer thickness for nanophotonics
Authors: Pan Chenxinyu , Tong Yuanbiao , Qian Haoliang , Krasavin Alexey V. , Li Jialin , Zhu Jiajie , Zhang Yiyun , Cui Bowen , Li Zhiyong , Wu Chenming , Liu Lufang , Li Linjun , Guo Xin , Zayats Anatoly V. , Tong Limin , Wang Pan 
Published: April 2024

AbstractTwo-dimensional single crystal metals, in which the behavior of highly confined optical modes is intertwined with quantum phenomena, are highly sought after for next-generation technologies. Here, we report large area (>104 μm2), single crystal two-dimensional gold flakes (2DGFs) with thicknesses down to a single nanometer level, employing an atomic-level precision chemical etching approach. The decrease of the thickness down to such scales leads to the quantization of the electronic states, endowing 2DGFs with quantum-confinement-augmented optical nonlinearity, particularly leading to more than two orders of magnitude enhancement in harmonic generation compared with their thick polycrystalline counterparts. The nanometer-scale thickness and single crystal quality makes 2DGFs a promising platform for realizing plasmonic nanostructures with nanoscale optical confinement. This is demonstrated by patterning 2DGFs into nanoribbon arrays, exhibiting strongly confined near infrared plasmonic resonances with high quality factors. The developed 2DGFs provide an emerging platform for nanophotonic research and open up opportunities for applications in ultrathin plasmonic, optoelectronic and quantum devices.
Extending carbon chemistry at high-pressure by synthesis of CaC2 and Ca3C7 with deprotonated polyacene- and para-poly(indenoindene)-like nanoribbons
Authors: Khandarkhaeva Saiana , Fedotenko Timofey , Aslandukova Alena , Akbar Fariia Iasmin , Bykov Maxim , Laniel Dominique , Aslandukov Andrey , Ruschewitz Uwe , Tobeck Christian , Winkler Björn , Chariton Stella , Prakapenka Vitali , Glazyrin Konstantin , Giacobbe Carlotta , Bright Eleanor Lawrence , Belov Maxim , Dubrovinskaia Natalia , Dubrovinsky Leonid 
Published: April 2024

AbstractMetal carbides are known to contain small carbon units similar to those found in the molecules of methane, acetylene, and allene. However, for numerous binary systems ab initio calculations predict the formation of unusual metal carbides with exotic polycarbon units, [C6] rings, and graphitic carbon sheets at high pressure (HP). Here we report the synthesis and structural characterization of a HP-CaC2 polymorph and a Ca3C7 compound featuring deprotonated polyacene-like and para-poly(indenoindene)-like nanoribbons, respectively. We also demonstrate that carbides with infinite chains of fused [C6] rings can exist even at conditions of deep planetary interiors ( ~ 140 GPa and ~3300 K). Hydrolysis of high-pressure carbides may provide a possible abiotic route to polycyclic aromatic hydrocarbons in Universe.
Incident allergic diseases in post-COVID-19 condition: multinational cohort studies from South Korea, Japan and the UK
Authors: Oh Jiyeon , Lee Myeongcheol , Kim Minji , Kim Hyeon Jin , Lee Seung Won , Rhee Sang Youl , Koyanagi Ai , Smith Lee , Kim Min Seo , Lee Hayeon , Lee Jinseok , Yon Dong Keon 
Published: April 2024

AbstractAs mounting evidence suggests a higher incidence of adverse consequences, such as disruption of the immune system, among patients with a history of COVID-19, we aimed to investigate post-COVID-19 conditions on a comprehensive set of allergic diseases including asthma, allergic rhinitis, atopic dermatitis, and food allergy. We used nationwide claims-based cohorts in South Korea (K-CoV-N; n = 836,164; main cohort) and Japan (JMDC; n = 2,541,021; replication cohort A) and the UK Biobank cohort (UKB; n = 325,843; replication cohort B) after 1:5 propensity score matching. Among the 836,164 individuals in the main cohort (mean age, 50.25 years [SD, 13.86]; 372,914 [44.6%] women), 147,824 were infected with SARS-CoV-2 during the follow-up period (2020−2021). The risk of developing allergic diseases, beyond the first 30 days of diagnosis of COVID-19, significantly increased (HR, 1.20; 95% CI, 1.13−1.27), notably in asthma (HR, 2.25; 95% CI, 1.80−2.83) and allergic rhinitis (HR, 1.23; 95% CI, 1.15−1.32). This risk gradually decreased over time, but it persisted throughout the follow-up period (≥6 months). In addition, the risk increased with increasing severity of COVID-19. Notably, COVID-19 vaccination of at least two doses had a protective effect against subsequent allergic diseases (HR, 0.81; 95% CI, 0.68−0.96). Similar findings were reported in the replication cohorts A and B. Although the potential for misclassification of pre-existing allergic conditions as incident diseases remains a limitation, ethnic diversity for evidence of incident allergic diseases in post-COVID-19 condition has been validated by utilizing multinational and independent population-based cohorts.
Hidden diversity and potential ecological function of phosphorus acquisition genes in widespread terrestrial bacteriophages
Authors: Liang Jie-Liang , Feng Shi-wei , Lu Jing-li , Wang Xiao-nan , Li Feng-lin , Guo Yu-qian , Liu Shen-yan , Zhuang Yuan-yue , Zhong Sheng-ji , Zheng Jin , Wen Ping , Yi Xinzhu , Jia Pu , Liao Bin , Shu Wen-sheng , Li Jin-tian 
Published: April 2024

AbstractPhosphorus (P) limitation of ecosystem processes is widespread in terrestrial habitats. While a few auxiliary metabolic genes (AMGs) in bacteriophages from aquatic habitats are reported to have the potential to enhance P-acquisition ability of their hosts, little is known about the diversity and potential ecological function of P-acquisition genes encoded by terrestrial bacteriophages. Here, we analyze 333 soil metagenomes from five terrestrial habitat types across China and identify 75 viral operational taxonomic units (vOTUs) that encode 105 P-acquisition AMGs. These AMGs span 17 distinct functional genes involved in four primary processes of microbial P-acquisition. Among them, over 60% (11/17) have not been reported previously. We experimentally verify in-vitro enzymatic activities of two pyrophosphatases and one alkaline phosphatase encoded by P-acquisition vOTUs. Thirty-six percent of the 75 P-acquisition vOTUs are detectable in a published global topsoil metagenome dataset. Further analyses reveal that, under certain circumstances, the identified P-acquisition AMGs have a greater influence on soil P availability and are more dominant in soil metatranscriptomes than their corresponding bacterial genes. Overall, our results reinforce the necessity of incorporating viral contributions into biogeochemical P cycling.
Monolithically integrated micro-supercapacitors with high areal number density produced by surface adhesive-directed electrolyte assembly
Authors: Wang Sen , Zheng Shuanghao , Shi Xiaoyu , Das Pratteek , Li Linmei , Zhu Yuanyuan , Lu Yao , Feng Xinliang , Wu Zhong-Shuai 
Published: April 2024

AbstractAccurately placing very small amounts of electrolyte on tiny micro-supercapacitors (MSCs) arrays in close proximity is a major challenge. This difficulty hinders the development of densely-compact monolithically integrated MSCs (MIMSCs). To overcome this grand challenge, we demonstrate a controllable electrolyte directed assembly strategy for precise isolation of densely-packed MSCs at micron scale, achieving scalable production of MIMSCs with ultrahigh areal number density and output voltage. We fabricate a patterned adhesive surface across MIMSCs, that induce electrolyte directed assembly on 10,000 highly adhesive MSC regions, achieving a 100 µm-scale spatial separation between each electrolyte droplet within seconds. The resultant MIMSCs achieve an areal number density of 210 cells cm−2 and a high areal voltage of 555 V cm−2. Further, cycling the MIMSCs at 190 V over 9000 times manifests no performance degradation. A seamlessly integrated system of ultracompact wirelessly-chargeable MIMSCs is also demonstrated to show its practicality and versatile applicability.
Reconstructing the evolution history of networked complex systems
Authors: Wang Junya , Zhang Yi-Jiao , Xu Cong , Li Jiaze , Sun Jiachen , Xie Jiarong , Feng Ling , Zhou Tianshou , Hu Yanqing 
Published: April 2024

AbstractThe evolution processes of complex systems carry key information in the systems’ functional properties. Applying machine learning algorithms, we demonstrate that the historical formation process of various networked complex systems can be extracted, including protein-protein interaction, ecology, and social network systems. The recovered evolution process has demonstrations of immense scientific values, such as interpreting the evolution of protein-protein interaction network, facilitating structure prediction, and particularly revealing the key co-evolution features of network structures such as preferential attachment, community structure, local clustering, degree-degree correlation that could not be explained collectively by previous theories. Intriguingly, we discover that for large networks, if the performance of the machine learning model is slightly better than a random guess on the pairwise order of links, reliable restoration of the overall network formation process can be achieved. This suggests that evolution history restoration is generally highly feasible on empirical networks.
Dynamic encoding of temperature in the central circadian circuit coordinates physiological activities
Authors: Li Hailiang , Li Zhiyi , Yuan Xin , Tian Yue , Ye Wenjing , Zeng Pengyu , Li Xiao-Ming , Guo Fang 
Published: April 2024

AbstractThe circadian clock regulates animal physiological activities. How temperature reorganizes circadian-dependent physiological activities remains elusive. Here, using in-vivo two-photon imaging with the temperature control device, we investigated the response of the Drosophila central circadian circuit to temperature variation and identified that DN1as serves as the most sensitive temperature-sensing neurons. The circadian clock gate DN1a’s diurnal temperature response. Trans-synaptic tracing, connectome analysis, and functional imaging data reveal that DN1as bidirectionally targets two circadian neuronal subsets: activity-related E cells and sleep-promoting DN3s. Specifically, behavioral data demonstrate that the DN1a-E cell circuit modulates the evening locomotion peak in response to cold temperature, while the DN1a-DN3 circuit controls the warm temperature-induced nocturnal sleep reduction. Our findings systematically and comprehensively illustrate how the central circadian circuit dynamically integrates temperature and light signals to effectively coordinate wakefulness and sleep at different times of the day, shedding light on the conserved neural mechanisms underlying temperature-regulated circadian physiology in animals.
Early detection of emerging viral variants through analysis of community structure of coordinated substitution networks
Authors: Mohebbi Fatemeh , Zelikovsky Alex , Mangul Serghei , Chowell Gerardo , Skums Pavel 
Published: April 2024

AbstractThe emergence of viral variants with altered phenotypes is a public health challenge underscoring the need for advanced evolutionary forecasting methods. Given extensive epistatic interactions within viral genomes and known viral evolutionary history, efficient genomic surveillance necessitates early detection of emerging viral haplotypes rather than commonly targeted single mutations. Haplotype inference, however, is a significantly more challenging problem precluding the use of traditional approaches. Here, using SARS-CoV-2 evolutionary dynamics as a case study, we show that emerging haplotypes with altered transmissibility can be linked to dense communities in coordinated substitution networks, which become discernible significantly earlier than the haplotypes become prevalent. From these insights, we develop a computational framework for inference of viral variants and validate it by successful early detection of known SARS-CoV-2 strains. Our methodology offers greater scalability than phylogenetic lineage tracing and can be applied to any rapidly evolving pathogen with adequate genomic surveillance data.
Obesity-related T cell dysfunction impairs immunosurveillance and increases cancer risk
Authors: Piening Alexander , Ebert Emily , Gottlieb Carter , Khojandi Niloufar , Kuehm Lindsey M. , Hoft Stella G. , Pyles Kelly D. , McCommis Kyle S. , DiPaolo Richard J. , Ferris Stephen T. , Alspach Elise , Teague Ryan M. 
Published: April 2024

AbstractObesity is a well-established risk factor for human cancer, yet the underlying mechanisms remain elusive. Immune dysfunction is commonly associated with obesity but whether compromised immune surveillance contributes to cancer susceptibility in individuals with obesity is unclear. Here we use a mouse model of diet-induced obesity to investigate tumor-infiltrating CD8 + T cell responses in lean, obese, and previously obese hosts that lost weight through either dietary restriction or treatment with semaglutide. While both strategies reduce body mass, only dietary intervention restores T cell function and improves responses to immunotherapy. In mice exposed to a chemical carcinogen, obesity-related immune dysfunction leads to higher incidence of sarcoma development. However, impaired immunoediting in the obese environment enhances tumor immunogenicity, making the malignancies highly sensitive to immunotherapy. These findings offer insight into the complex interplay between obesity, immunity and cancer, and provide explanation for the obesity paradox observed in clinical immunotherapy settings.
Physics-inspired transfer learning for ML-prediction of CNT band gaps from limited data
Authors: Bets Ksenia V. , O’Driscoll Patrick C. , Yakobson Boris I. 
Published: April 2024

AbstractRecent years have seen a drastic increase in the scientific use of machine learning (ML) techniques, yet their applications remain limited for many fields. Here, we demonstrate techniques that allow overcoming two obstacles to the widespread adoption of ML, particularly relevant to nanomaterials and nanoscience fields. Using the prediction of the band gap values of carbon nanotubes as a typical example, we address the representation of the periodic data as well as training on extremely small datasets. We successfully showed that careful choice of the activation function allows capturing periodic tendencies in the datasets that are common in physical data and previously posed significant difficulty for neural networks. In particular, utilization of the recently proposed parametric periodic Snake activation function shows a dramatic improvement. Furthermore, tackling a typical lack of accurate data, we used the transfer learning technique utilizing more abundant low-quality computational data and achieving outstanding accuracy on a significantly expanded dataspace. This strategy was enabled by the use of a combination of the Snake and ReLU layers, capturing data periodicity and amplitude, respectively. Hence, retraining only ReLU layers allowed the transfer of the periodic tendencies captured from low-quality data to the final high-accuracy neural network. Those techniques are expected to expand the usability of ML approaches in application to physical data in general and the fields of nanomaterials in particular.
Model-based financial regulations impair the transition to net-zero carbon emissions
Authors: Gasparini Matteo , Ives Matthew C. , Carr Ben , Fry Sophie , Beinhocker Eric 
Published: April 2024

AbstractInvestments via the financial system are essential for fostering the green transition. However, the role of existing financial regulations in influencing investment decisions is understudied. Here we analyse data from the European Banking Authority to show that existing financial accounting frameworks might inadvertently be creating disincentives for investments in low-carbon assets. We find that differences in the provision coverage ratio indicate that banks must account for nearly double the loan loss provisions for lending to low-carbon sectors as compared with high-carbon sectors. This bias is probably the result of basing risk estimates on historical data. We show that the average historical financial risk of the oil and gas sector has been consistently estimated to be lower than that of renewable energy. These results indicate that this bias could be present in other model-based regulations, such as capital requirements, and possibly impact the ability of banks to fund green investments.
SARS-CoV-2 variant with the spike protein mutation F306L in the southern border provinces of Thailand
Authors: Sila Thanit , Surasombatpattana Smonrapat , Rajborirug Songyos , Laochareonsuk Wison , Choochuen Pongsakorn , Kongkamol Chanon , Ingviya Thammasin , Prompat Napat , Mahasirimongkol Surakameth , Sangkhathat Surasak , Aiewsakun Pakorn 
Published: April 2024

AbstractThe southernmost part of Thailand is a unique and culturally diverse region that has been greatly affected by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) outbreak during the coronavirus disease-2019 pandemic. To gain insights into this situation, we analyzed 1942 whole-genome sequences of SARS-CoV-2 obtained from the five southernmost provinces of Thailand between April 2021 and March 2022, together with those publicly available in the Global Initiative on Sharing All Influenza Data database. Our analysis revealed evidence for transboundary transmissions of the virus in and out of the five southernmost provinces during the study period, from both domestic and international sources. The most prevalent viral variant in our sequence dataset was the Delta B.1.617.2.85 variant, also known as the Delta AY.85 variant, with many samples carrying a non-synonymous mutation F306L in their spike protein. Protein–protein docking and binding interface analyses suggested that the mutation may enhance the binding between the spike protein and host cell receptor protein angiotensin-converting enzyme 2, and we found that the mutation was significantly associated with an increased fatality rate. This mutation has also been observed in other SARS-CoV-2 variants, suggesting that it is of particular interest and should be monitored.
Prediction model for spinal cord injury in spinal tuberculosis patients using multiple machine learning algorithms: a multicentric study
Authors: Feng Sitan , Wang Shujiang , Liu Chong , Wu Shaofeng , Zhang Bin , Lu Chunxian , Huang Chengqian , Chen Tianyou , Zhou Chenxing , Zhu Jichong , Chen Jiarui , Xue Jiang , Wei Wendi , Zhan Xinli 
Published: April 2024

AbstractSpinal cord injury (SCI) is a prevalent and serious complication among patients with spinal tuberculosis (STB) that can lead to motor and sensory impairment and potentially paraplegia. This research aims to identify factors associated with SCI in STB patients and to develop a clinically significant predictive model. Clinical data from STB patients at a single hospital were collected and divided into training and validation sets. Univariate analysis was employed to screen clinical indicators in the training set. Multiple machine learning (ML) algorithms were utilized to establish predictive models. Model performance was evaluated and compared using receiver operating characteristic (ROC) curves, area under the curve (AUC), calibration curve analysis, decision curve analysis (DCA), and precision-recall (PR) curves. The optimal model was determined, and a prospective cohort from two other hospitals served as a testing set to assess its accuracy. Model interpretation and variable importance ranking were conducted using the DALEX R package. The model was deployed on the web by using the Shiny app. Ten clinical characteristics were utilized for the model. The random forest (RF) model emerged as the optimal choice based on the AUC, PRs, calibration curve analysis, and DCA, achieving a test set AUC of 0.816. Additionally, MONO was identified as the primary predictor of SCI in STB patients through variable importance ranking. The RF predictive model provides an efficient and swift approach for predicting SCI in STB patients.