bluuebunny/crossref_metadata_2025_split huggingface dataset:
Structural insights into the mechanism of phosphate recognition and transport by human XPR1
Authors: Liu Zhu , Zhang Wenhui , Chen Yanke , Guan Zeyuan , Du Zhangmeng , Cheng Meng , Zhang Jie , Zuo Jiaqi , Cheng Peng , Wang Qiang , Liu Yanjun , Zhang Delin , Yin Ping 
Published: August 2024

Abstract

XPR1 is the only known protein that transports inorganic phosphate (Pi) out of cells, and the function is conserved across species from yeast to mammals1-4. Human XPR1 variants lead to cerebral calcium-phosphate deposition, which are associated with a neurodegenerative disorder known as primary familial brain calcification (PFBC)5. Here, we present the Cryo-EM structure of human XPR1 bound to Pi ions. XPR1 contains 10 transmembrane α-helices, forming an ion channel-like architecture that recognizes and transports Pi ions. Two arginine residues, subject to pathogenic mutation in PFBC families, line the translocation channel and serve to bind Pi ions. Clinically linked mutations of these arginine residues impair the Pi transport activity of XPR1. To track the movement of Pi ions within the translocation channel, we capture a mutant XPR1 in an alternative conformation. It reveals a rearrangement of intrahelical hydrogen bonds between a channel-lining tryptophan and two Pi-binding residues. This rearrangement links Pi recognition and transport, by means of flipping the tryptophan residue to propel Pi through the translocation channel. Our results provide mechanistic understanding of how XPR1 recognizes and transports phosphate ions across cell membrane, and they establish a framework for interpreting disease-related mutations and for the development of future therapeutics.
Virulence Plasmid with IroBCDN Deletion Promoted Cross-regional Transmission of ST11-KL64 Carbapenem-resistant Hypervirulent Klebsiella Pneumoniae in central China
Authors: Hong Han-xu , Huo Bing-Hui , Xu Ying , Xiang Tian-Xin , Wei Dan-Dan , Huang Qi-Sen , Liu Peng , Zhang Wei , Liu Yang 
Published: August 2024

Abstract

Background:Carbapenem-resistant and hypervirulent Klebsiella pneumoniae (CR-hvKP) caused infections of high mortality and brought a serious impact on public health. This study evaluated the transmissibility of CR-hvKP among different regions to provide corresponding advice for cross-regional prevention.
Methods: Clinical K. pneumoniae strains were collected from Jiujiang region and Nanchang region in Jiangxi province from November 2021 to June 2022. We characterized these strains for their genetic relatedness, virulence phenotype, and dissimilarities in virulence plasmid structures using PFGE, Galleria mellonella, Whole-genome sequencing.
Results: Among 609 strains, 45 (7.4%) CR-hvKP were identified, while the strains
isolated from Nanchang and Jiujiang accounted for 10.05% (36/358) and 3.59% (9/251). We observed that ST11-KL64 CR-hvKP had an overwhelming epidemic dominance in these two regions. Significant genetic diversity was identified among all ST11-KL64 CR-hvKP cross-regional transmission between Nanchang and Jiujiang serving as major drivers of clonal clusters. Virulence genes profile revealed that ST11-KL64 CR-hvKP might harbour incomplete pLVPK-like plasmids and primarily evolved from CRKP by acquiring the hypervirulence plasmid. We found the predominance of truncated-IncFIB/IncHI1B type virulence plasmids with a 25kb fragment deletion that encoded iroBCDN clusters.
Conclusion: ST11-KL64 is the most cross-regional prevalent type CR-hvKPs in Jiangxi province, which mainly evolved from CRKPs by acquiring a truncated-IncHI1B/IncFIB virulence plasmid with the deletion of iroBCDN. Stricter surveillance and control measures are urgently needed to prevent the epidemic transmission of ST11-KL64 CR-hvKP.
Faxonius rusticus impact on the biodiversity of benthic macroinvertebrate assemblages in the Conodoguinet Creek
Authors: Carmichael Madeleine 
Published: August 2024

Abstract

The Rusty Crawdad (Faxonius rusticus) is a species invasive to the streams of Pennsylvania, chiefly the Conodoguinet Creek. However, little research has been done to investigate Faxonius rusticus’ impact on the biodiversity of the Conodoguinet’s particular ecosystem. The biodiversity of benthic macroinvertebrates is often used to determine the health of lotic ecosystems and as such was tested in order to analyze the mechanisms through which Faxonius rusticus may be altering the creek’s food web. Although the results exhibited a degree of discordance, data collected via this method suggested that biodiversity of the Conodoguinet was generally of good quality despite the presence of Faxonius rusticus. Thus, it was speculated that Faxonius rusticusmay be beneficial to the Conodoguinet Creek ecosystem due to its predation on another invasive species residing in the Conodoguinet: the Asian clam (Corbicula fluminea). Faxonius rusticus may have been able to uphold the biodiversity of this ecosystem’s benthic macroinvertebrate assemblages by precluding the explosive growth of Corbicula fluminea populations and thus allowing native species to compete. As a result, this study suggests that invasive species, such as Faxonius rusticus, may have complex effects on biodiversity varying by ecosystems’ specific food webs.
The optimal dose of human umbilical-cord mesenchymal stem cells (hUC-MSCs) on hypoxic ischemic brain damage(HIBD) rats via intranasal transplantation
Authors: Yang Mengxue , Zheng Kun , Zhang R , He Guangzhen , Xu Jin , Jiang Hao , Ren La , Zhao Liang , Liao Yan , Fu Zeqin , Liu Wenting , Gu Jiaowei 
Published: August 2024

Abstract

Background: Hypoxic ischemic brain damage(HIBD) cause permanent damage to the central nervous system (CNS), including mental retardation, aphasia, epilepsy, cerebral palsy, and developmental delays. Recently, human umbilical cord mesenchymal stem cells (hUC-MSCs) have emerged as a promising cell therapy in HIBD. The optimal dose of stem cell therapy remains open.
Methods: Previously, we conducted a study to identify that the best route of administration of hUC-MSCs to HIBD rat model is intranasal transplantation.
In this study, we conducted the comparison experiments for the optimal dose of MSC via the best route of administration, intranasal transplantation. Four different doses (0.5*106, 1.0*106, 1.5*106, 2.0*106) of hUC-MSCs were injected to HIBD rats (obtained HIBD at 7th day after birth) on the10th day after birth.
The body weight, hanging wire test and Morris Water Maze test were used on the 28th day of different groups of rats to compare their motor function, memory, and learning ability. The HE staining was used to compare the morphological differences of brain tissues of rats in different groups. Moreover, the immunohistochemistry was adopted to compare the morphology and number of astrocytes and microglia in cortex and CA1, CA3 region of the brain of different groups.
Results: All the four different doses of hUC-MSCs treatment significantly improved their body weight, the motor function, memory, learning ability, the morphology and number of astrocytes and microglia in cortex and CA1, CA3 region of HIBD rats. The 1.5*106 of hUC-MSCs group significantly outperform other three groups (0.5*106, 1.0*106, 2.0*106) on both neurobehavioral motor function improvement and morphological improvement, i.e., reducing the number of astrocytes and microglia in cortex and CA1, CA3 region of HIBD rats. The study suggests that 1.5*106 of hUC-MSCs was the optimal dose for rats who obtained HIBD at 7th day after birth and injected by intranasal transplantation on the 10th day after birth.
Ovarian treatment with Autologous Platelet-Rich Plasma plus autologous Bone Marrow-derived Stem cells. Case series
Authors: Alzogaray Victoria , Orihuela Ana , Mazza Milton , Vernocchi Rita , Guidobono Martha , Buzzi Juan Rodriguez , Novick Wanda 
Published: August 2024

Abstract

Introduction: There are limited treatment options for women with severely diminished ovarian reserve (DOR) who experience repeatedly failed in vitro fertilization (IVF) cycles. Therefore, most patients resort to using donor oocytes. Ovarian rejuvenation is an innovative procedure intended to reactivate the remaining follicles in the ovary to improve ovarian response in assisted reproduction treatments. Case presentation: Using two treatments at the same time, we enhance the effects of intraovarian platelet-rich plasma (PRP) injection and Autologous Bone Marrow-Derived Mesenchymal Stem Cells on ovarian stimulation outcomes in three women referred to a Uruguayan IVF center. Conclusion: Data presented herein suggest that autologous intraovarian PRP infusion plus Stem Cells may restore ovarian function. This may enable the achievement of pregnancy. However, the occurrence of spontaneous pregnancy, clinical pregnancy and live birth reflect a significant change. Longer follow-up may detect a functional recovery with improvement of the ovarian reserve hormone markers.
FTO suppresses cardiac fibrosis after myocardial infarction via m 6 A-mediated epigenetic modification of EPRS
Authors: Wang Jian , Li Yanyan , Deng Lijie , Zha Yafang , Zhang Song 
Published: August 2024

Abstract

Background
Cardiac fibrosis is common in cardiovascular diseases. Previous research has suggested that m6A modification is vital in cardiovascular diseases. However, the effects of m6A demethylase fat mass and obesity protein (FTO) on cardiac fibrosis have not been detected.
Methods
Western blot and Quantitative real-time PCR were performed to detect the expression of FTO in the fibrotic tissue of rats and hypoxia-induced cardiac fibroblasts (CFs). The HIF-1 signal pathway was enriched, and a CUT&Tag assay was performed to explore the effects on FTO. The biological functions of FTO were analyzed in vitro and in vivo. The underlying targets of FTO were selected through RNA-seq with m6A-seq. The following dual luciferase reporter assay and RNA stability assay were conducted to investigate the mechanisms of FTO-mediated m6A regulation.
Results
The expression of FTO was decreased in the fibrotic tissue of rats and hypoxia-induced CFs. The knockdown of HIF-1α, instead of HIF-2α, increased the mRNA level of FTO. HIF-1α could bind to the promoter of FTO and inhibit FTO expression. FTO inhibited collagen synthesis in CFs. According to m6A-seq and RNA-seq, EPRS was selected as the underlying target of FTO-induced m6A regulation. IGF2BP3 recognized and bound to the m6A sites of EPRS mRNA, which improved its stability. EPRS was required for cardiac fibrosis induced by FTO silencing.
Conclusions
HIF-1α induced by MI bound to the FTO promoter and inhibited FTO expression. FTO suppressed collagen synthesis of CFs in cardiac fibrosis via m6A-mediated modification, which provided a new therapeutic strategy for cardiac fibrosis.
Brain 18FDG-PET pattern in cognitively impaired elderly patients with bipolar disorder
Authors: Saleh Nouredine , Blaise Capucine , Daoudi Amina , Queneau Matthieu , Fard Karim , Dumurgier Julien , Munoz-Musat Esteban , Marlinge Emeline , Hugon Jacques , Hourregue Claire , Paquet Claire , Cognat Emmanuel 
Published: August 2024

Abstract

Background
Patients with bipolar disorder (BD) are at increased risk of dementia. The underlying mechanisms are debated. This study aims to investigate cerebral glucose metabolism in cognitively impaired elderly BD using FDG-PET imaging, to elucidate potential underlying mechanisms and improve diagnostic accuracy.
Methods
We conducted a retrospective analysis of FDG-PET scans from 32 cognitively impaired elderly patients with BD (mean age 70.4 years). These were compared with scans from 35 neurocognitive controls (NCC) and patients diagnosed with Alzheimer’s disease (AD, n = 27), frontotemporal dementia (FTD, n = 26), and dementia with Lewy bodies (DLB, n = 18). Voxel-wise statistical analysis was performed using SPM software, adjusting for age and sex.
Results
No significant cortical hypometabolism was found in BD patients compared to NCC. In contrast, typical patterns of hypometabolism were observed in the AD, FTD, and DLB groups. The findings suggest that late-life cognitive impairment in BD is not due to a single common neurodegenerative process.
Conclusion
The absence of abnormal cortical metabolism in cognitively impaired elderly patients with BD suggests that cognitive impairment in this population may not be driven by a common neurodegenerative pathway. Further studies using other biomarkers are needed to investigate the brain processes involved, which could lead to improved understanding and management of cognitive impairment in BD.
Using pathogen genomics to predict antigenic changes of influenza H3N2 virus
Authors: Wang Lin , Yao Sidan , Pei Yao , Huang Angkana , lefrancq Noemie , Sun Kaiyuan , Du Zhanwei , Luo Wei , Han Alvin , Liu Yong , Goh Rick Siow Mong , Xu Yani , Lee Raphael , Russell Colin , Cauchemez Simon , Maurer-Stroh Sebastian , Luo Tao , Salje Henrik 
Published: August 2024

Abstract

Influenza viruses evolve their antigenic phenotype in response to immune-driven selection, necessitating regular updates to the antigens used in influenza vaccines. Characterising antigenic change of influenza virus currently requires intensive serology-based laboratory work to analyse the movements of viruses in antigenic space. By contrast, pathogen genomics can generate large numbers of sequences in near real-time. However, using genetic sequences to predict antigenic changes remains challenging due to the complex relationship between genetic and antigenic changes. Here we develop a deep learning approach to predict the antigenic distance between influenza H3N2 viruses using 83,145 hemagglutinin (HA) sequences. We first train an elementary protein language model to encode the HA sequences, and then couple the trained language model with a deep regression model to realise the sequence-based prediction. Our method provides stable and accurate prediction on the antigenic distance between H3N2 viruses using their HA sequences. This facilitates the prediction of antigenic movements of viruses in each influenza season using their sequence data. Our sequence-based method will enable real-time prediction of influenza antigenic changes and support vaccine strain selection.
Integration of transcriptomic and metabolomic data reveals key regulatory defense pathways associated with high temperature stress in cucumber (Cucumis sativus)
Authors: Yuan Yong , Ma Xiao , Li Chuang , Zhong Xitong , Li Yuyan , Zhao Jianyu , Zhang Xiaolan , Zhou Zhaoyang 
Published: August 2024

Abstract

High temperature stress seriously affects the quality and yield of vegetable crops, especially cucumber. However, the metabolic dynamics and underlying regulatory network of cucumber response to high temperature stress remain poorly studied. In this study, we identified a heat-tolerant cucumber Gy14 and a heat-sensitive cucumber 32X. RNA-seq analysis of Gy14 and 32X under high temperature stress showed that some differentially expressed genes (DEGs) were related to the biosynthesis of secondary metabolites. Metabolomic analysis revealed that there were more phenylpropanoids and their downstream derivatives in Gy14 compared to 32X under HS_R2 condition (2 normal days recovery after heat). Integrated analysis of transcriptome and metabolome revealed that the upregulation of genes plays a pivotal role in flavonoid biosynthesis. Moreover, high temperature stress significantly induced the expression of the gibberellin biosynthesis genes and exogenous application of GA3 alleviated the damage high temperature to cucumber seedlings. Together, these findings provided new insights into the transcriptome response and metabolomic reprogramming of cucumber against high temperature stress.
Deep learning PM 2.5 hybrid prediction model based on clustering- secondary decomposition strategy
Authors: Zeng Tao , Liu Yahui , Liu Ruru , Shi Jinli , luo Tao , Xi Yunyun , Zhao Shuo , Chen Chunpeng , Pan Guangrui , Zhou Yuming , Xu Liping 
Published: August 2024

Abstract

Accurate prediction of PM2.5 concentration is important for pollution control, public health and ecological protection. To this end, this paper proposes a deep learning hybrid prediction model based on clustering and secondary decomposition, aiming to achieve accurate prediction of PM2.5 concentration. The model utilizes the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to decompose the PM2.5 sequences into multiple intrinsic modal function components (IMFs), and clusters and re-fuses the sub-sequences with similar complexity by permutation entropy (PE) and K-means clustering. For the fused high-frequency sequences a secondary decomposition is performed using the whale optimization algorithm (WOA) optimized variational modal decomposition (VMD). Finally, prediction is performed using the two basic frameworks combined with the long and short-term memory neural network (LSTM). Experiments show that this proposed model exhibits good stability and generalization ability. It does not only make accurate predicts in the short term, but also captures the trends in the long-term prediction. There is a significant performance improvement over the four deep learning baseline models. Further comparisons with existing models outperform the current state-of-the-art models.
Assessing the Efficacy of IoT-driven Machine Learning Models in Enhancing Chili Crop Growth and Yield Quality
Authors: Kodukula Subrahmanyam , Vidyullatha P. , Ghantasala G S Pradeep , Vysali P , Dubba Nagamalleswary , Nassar Ibrahim 
Published: August 2024

Abstract

This study analyses the plant growth and yield parameters in drip-irrigated and furrow irrigated chili fields in the Guntur region during Kharif 2021. As a part of the DST-SEED project, 48 farmers' chili crops were selected amongst 21 clusters to collect data on soil characteristics, fruits of each plant, their height, fruit length, and fruit yield. The present comparative study purposes to establish the growth and quality of chili fields in the selected drip-irrigated areas. The study's findings show that the approach of drip irrigation integrated with the Internet of Things (IoT) devices excelled in all parameters studied over the furrow irrigation method. The maximum and minimum plant heights in a drip-irrigated farmer's field were 102.3 and 77.46 cm, respectively. While furrow irrigated, farmer's fields produced plant heights of 93.3 and 70.3 cm, respectively. The maximum and minimum number of plant-1 fruits and fruit lengths are 110 and 81.5, 8.1, and 5.9 cm were recorded in the drip-irrigated farmer's field integrated with IoT devices. IoT devices were placed to control the water flow smartly through a mobile app. At the same time, furrow-irrigated farmer's fields could produce the lowest yield of fruits in plant-1; the fruit lengths (cm) are 94.57 and 70.21, 6.7 and 4.7 cm, respectively. The current research recommends that agricultural communities use drip irrigation integrated with IoT devices instead of the old conventional flooding techniques, assess the nutrient state of their soil, implement the indicated logical nutrient management practices, smart motor control, and plant high-yielding varieties or hybrids. This research underscores the importance of adopting modern agricultural techniques, including drip irrigation integrated with IoT devices and machine learning-based predictive models, for enhancing chili yield growth.
A systematic review on One-Time Programs
Authors: Casciaro Simone , Mainetti Luca , Vergallo Roberto 
Published: August 2024

Abstract

One-Time Program (OTPr) is a cryptographic construct designed to ensure the single execution of a given computer program. In their early theoretical conception in 2008, OTPrs were defined as a new paradigm in data privacy and information security scenarios. Over the years, several practical approaches — both hardware and software-based — have been proposed: garbled circuits, One-Time Memory (OTM), cryptographic algorithms, Trusted Execution Environment (TEE), and Blockchain systems. Moreover, methods based on quantum computing and quantum properties have been recently added to the frame. Nonetheless, a comprehensive overview of OTPrs is still missing up to this date. In this article, we present a systematic review of OTPrs literature, composed of 42 relevant articles. The primary studies explore theoretical foundations, practical implementations, and new possible applications of OTPrs across various domains. Our work provides researchers and software engineers with a complete picture of possible OTPrs components, and suggests a holistic methodology for experimenting with this new computing paradigm. Results show that OTPrs can find applications in novel scenarios responding to some of the most recent needs of both consumers and industry, despite the complexity of reproducing and applying such technology in the consumer market. Finally, an analysis of current research challenges is provided, which represents a starting point for researchers who want to contribute to the field.
Clustering of Renewable Energy Assets to Optimize Resource Allocation and Operational Strategies
Authors: Abreu Sara , Rodrigues Fátima , Pereira João 
Published: August 2024

Abstract

This study clusters solar inverters and wind turbines to help Enlitia’s clients optimize resource allocation and operational strategies by identifying similar assets based on historical power production, meteorological data, and power curve characteristics. The project employs Data Mining techniques following the CRISP-DM methodology, emphasizing data cleaning to handle null values, duplicates , and outliers. For wind turbines, outliers are managed using power curves, while for solar inverters, I-V curves are utilized.
Clustering begins after data cleaning, using algorithms from classical, ensemble, and time series clustering categories. Principal Component Analysis is applied to reduce computational costs while preserving significant data variation. Classical clustering involves five hierarchical, two partitional, one soft, one model-based, and two density-based algorithms, evaluated using four distinct indices. The top three classical algorithms proceed to ensemble clustering, combining the three algorithms via weighted major voting. Lastly, two time series clustering algorithms are applied to pre-processed datasets.
Evaluation of segmentations indicates that time is a significant factor in data variation. Time series clustering consistently produces the best segmentations for both solar and wind datasets.