bluuebunny/crossref_metadata_2025_split huggingface dataset:
Asymmetric transformation of achiral gold nanoclusters with negative nonlinear dependence between chiroptical activity and enantiomeric excess
Authors: Liu Chang , Zhao Yan , Zhang Tai-Song , Tao Cheng-Bo , Fei Wenwen , Zhang Sheng , Li Man-Bo 
Published: June 2023

AbstractThe investigation of chirality at the nanoscale is important to bridge the gap between molecular and macroscopic chirality. Atomically precise metal nanoclusters provide an ideal platform for this research, while their enantiopure preparation poses a challenge. Here, we describe an efficient approach to enantiopure metal nanoclusters via asymmetric transformation, that is, achiral Au23(SC6H11)16 nanoclusters are converted into chiral and enantiopure Au24(L)2(SC6H11)16 nanoclusters by a chiral inducer phosphoramidite (L). Two enantiomers of Au24(L)2(SC6H11)16 are obtained and the crystal structures reveal their hierarchical chirality, which originates from the two introduced chiral L molecules, the transformation-triggered asymmetric rearrangement of the staple motifs on the surface of the gold core, and the helical arrangement of nanocluster molecules. The construction of this type of enantiomerically pure nanoclusters is achieved based on the easy-to-synthesize and modular L. Lastly, the chirality-related chiroptical performance was investigated, revealing a negative nonlinear CD-ee dependence.
The ER calcium channel Csg2 integrates sphingolipid metabolism with autophagy
Authors: Liu Shiyan , Chen Mutian , Wang Yichang , Lei Yuqing , Huang Ting , Zhang Yabin , Lam Sin Man , Li Huihui , Qi Shiqian , Geng Jia , Lu Kefeng 
Published: June 2023

AbstractSphingolipids are ubiquitous components of membranes and function as bioactive lipid signaling molecules. Here, through genetic screening and lipidomics analyses, we find that the endoplasmic reticulum (ER) calcium channel Csg2 integrates sphingolipid metabolism with autophagy by regulating ER calcium homeostasis in the yeastSaccharomyces cerevisiae. Csg2 functions as a calcium release channel and maintains calcium homeostasis in the ER, which enables normal functioning of the essential sphingolipid synthase Aur1. Under starvation conditions, deletion of Csg2 causes increases in calcium levels in the ER and then disturbs Aur1 stability, leading to accumulation of the bioactive sphingolipid phytosphingosine, which specifically and completely blocks autophagy and induces loss of starvation resistance in cells. Our findings indicate that calcium homeostasis in the ER mediated by the channel Csg2 translates sphingolipid metabolism into autophagy regulation, further supporting the role of the ER as a signaling hub for calcium homeostasis, sphingolipid metabolism and autophagy.
Anxiety in synucleinopathies: neuronal circuitry, underlying pathomechanisms and current therapeutic strategies
Authors: Lai Thuy Thi , Gericke Birthe , Feja Malte , Conoscenti Michael , Zelikowsky Moriel , Richter Franziska 
Published: June 2023

AbstractSynucleinopathies are neurodegenerative disorders characterized by alpha-synuclein (αSyn) accumulation in neurons or glial cells, including Parkinson’s disease (PD), dementia with Lewy bodies (DLB), and multiple system atrophy (MSA). αSyn-related pathology plays a critical role in the pathogenesis of synucleinopathies leading to the progressive loss of neuronal populations in specific brain regions and the development of motor and non-motor symptoms. Anxiety is among the most frequent non-motor symptoms in patients with PD, but it remains underrecognized and undertreated, which significantly reduces the quality of life for patients. Anxiety is defined as a neuropsychiatric complication with characteristics such as nervousness, loss of concentration, and sweating due to the anticipation of impending danger. In patients with PD, neuropathology in the amygdala, a central region in the anxiety and fear circuitry, may contribute to the high prevalence of anxiety. Studies in animal models reported αSyn pathology in the amygdala together with alteration of anxiety or fear learning response. Therefore, understanding the progression, extent, and specifics of pathology in the anxiety and fear circuitry in synucleinopathies will suggest novel approaches to the diagnosis and treatment of neuropsychiatric symptoms. Here, we provide an overview of studies that address neuropsychiatric symptoms in synucleinopathies. We offer insights into anxiety and fear circuitry in animal models and the current implications for therapeutic intervention. In summary, it is apparent that anxiety is not a bystander symptom in these disorders but reflects early pathogenic mechanisms in the cortico-limbic system which may even contribute as a driver to disease progression.
Genomes of fungi and relatives reveal delayed loss of ancestral gene families and evolution of key fungal traits
Authors: Merényi Zsolt , Krizsán Krisztina , Sahu Neha , Liu Xiao-Bin , Bálint Balázs , Stajich Jason E. , Spatafora Joseph W. , Nagy László G. 
Published: June 2023

AbstractFungi are ecologically important heterotrophs that have radiated into most niches on Earth and fulfil key ecological services. Despite intense interest in their origins, major genomic trends of their evolutionary route from a unicellular opisthokont ancestor to derived multicellular fungi remain poorly known. Here we provide a highly resolved genome-wide catalogue of gene family changes across fungal evolution inferred from the genomes of 123 fungi and relatives. We show that a dominant trend in early fungal evolution has been the gradual shedding of protist genes and the punctuated emergence of innovation by two main gene duplication events. We find that the gene content of non-Dikarya fungi resembles that of unicellular opisthokonts in many respects, owing to the conservation of protist genes in their genomes. The most rapidly duplicating gene groups included extracellular proteins and transcription factors, as well as ones linked to the coordination of nutrient uptake with growth, highlighting the transition to a sessile osmotrophic feeding strategy and subsequent lifestyle evolution as important elements of early fungal history. These results suggest that the genomes of pre-fungal ancestors evolved into the typical filamentous fungal genome by a combination of gradual gene loss, turnover and several large duplication events rather than by abrupt changes. Consequently, the taxonomically defined Fungi represents a genomically non-uniform assemblage of species.
Intrinsic strong light-matter coupling with self-hybridized bound states in the continuum in van der Waals metasurfaces
Authors: Weber Thomas , Kühner Lucca , Sortino Luca , Ben Mhenni Amine , Wilson Nathan P. , Kühne Julius , Finley Jonathan J. , Maier Stefan A. , Tittl Andreas 
Published: June 2023

AbstractPhotonic bound states in the continuum (BICs) provide a standout platform for strong light-matter coupling with transition metal dichalcogenides (TMDCs) but have so far mostly been implemented as traditional all-dielectric metasurfaces with adjacent TMDC layers, incurring limitations related to strain, mode overlap and material integration. Here, we demonstrate intrinsic strong coupling in BIC-driven metasurfaces composed of nanostructured bulk tungsten disulfide (WS2) and exhibiting resonances with sharp, tailored linewidths and selective enhancement of light-matter interactions. Tuning of the BIC resonances across the exciton resonance in bulk WS2 is achieved by varying the metasurface unit cells, enabling strong coupling with an anticrossing pattern and a Rabi splitting of 116 meV. Crucially, the coupling strength itself can be controlled and is shown to be independent of material-intrinsic losses. Our self-hybridized metasurface platform can readily incorporate other TMDCs or excitonic materials to deliver fundamental insights and practical device concepts for polaritonic applications.
Modelling viral encephalitis caused by herpes simplex virus 1 infection in cerebral organoids
Authors: Rybak-Wolf Agnieszka , Wyler Emanuel , Pentimalli Tancredi Massimo , Legnini Ivano , Oliveras Martinez Anna , Glažar Petar , Loewa Anna , Kim Seung Joon , Kaufer Benedikt B. , Woehler Andrew , Landthaler Markus , Rajewsky Nikolaus 
Published: June 2023

AbstractHerpes simplex encephalitis is a life-threatening disease of the central nervous system caused by herpes simplex viruses (HSVs). Following standard of care with antiviral acyclovir treatment, most patients still experience various neurological sequelae. Here we characterize HSV-1 infection of human brain organoids by combining single-cell RNA sequencing, electrophysiology and immunostaining. We observed strong perturbations of tissue integrity, neuronal function and cellular transcriptomes. Under acyclovir treatment viral replication was stopped, but did not prevent HSV-1-driven defects such as damage of neuronal processes and neuroepithelium. Unbiased analysis of pathways deregulated upon infection revealed tumour necrosis factor activation as a potential causal factor. Combination of anti-inflammatory drugs such as necrostatin-1 or bardoxolone methyl with antiviral treatment prevented the damages caused by infection, indicating that tuning the inflammatory response in acute infection may improve current therapeutic strategies.
An Observation-Based Dataset of Global Sub-Daily Precipitation Indices (GSDR-I)
Authors: Pritchard David , Lewis Elizabeth , Blenkinsop Stephen , Patino Velasquez Luis , Whitford Anna , Fowler Hayley J. 
Published: June 2023

AbstractPrecipitation indices based on daily gauge observations are well established, openly available and widely used to detect and understand climate change. However, in many areas of climate science and risk management, it has become increasingly important to understand precipitation characteristics, variability and extremes at shorter (sub-daily) durations. Yet, no unified dataset of sub-daily indices has previously been available, due in large part to the lesser availability of suitable observations. Following extensive efforts in data collection and quality control, this study presents a new global dataset of sub-daily precipitation indices calculated from a unique database of 18,591 gauge time series. Developed together with prospective users, the indices describe sub-daily precipitation variability and extremes in terms of intensity, duration and frequency properties. The indices are published for each gauge where possible, alongside a gridded data product based on all gauges. The dataset will be useful in many fields concerned with variability and extremes in the climate system, as well as in climate model evaluation and management of floods and other risks.
A Database of Snow on Sea Ice in the Central Arctic Collected during the MOSAiC expedition
Authors: Macfarlane Amy R. , Schneebeli Martin , Dadic Ruzica , Tavri Aikaterini , Immerz Antonia , Polashenski Chris , Krampe Daniela , Clemens-Sewall David , Wagner David N. , Perovich Donald K. , Henna-Reetta Hannula , Raphael Ian , Matero Ilkka , Regnery Julia , Smith Madison M. , Nicolaus Marcel , Jaggi Matthias , Oggier Marc , Webster Melinda A. , Lehning Michael , Kolabutin Nikolai , Itkin Polona , Naderpour Reza , Pirazzini Roberta , Hämmerle Stefan , Arndt Stefanie , Fons Steven 
Published: June 2023

AbstractSnow plays an essential role in the Arctic as the interface between the sea ice and the atmosphere. Optical properties, thermal conductivity and mass distribution are critical to understanding the complex Arctic sea ice system’s energy balance and mass distribution. By conducting measurements from October 2019 to September 2020 on the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition, we have produced a dataset capturing the year-long evolution of the physical properties of the snow and surface scattering layer, a highly porous surface layer on Arctic sea ice that evolves due to preferential melt at the ice grain boundaries. The dataset includes measurements of snow during MOSAiC. Measurements included profiles of depth, density, temperature, snow water equivalent, penetration resistance, stable water isotope, salinity and microcomputer tomography samples. Most snowpit sites were visited and measured weekly to capture the temporal evolution of the physical properties of snow. The compiled dataset includes 576 snowpits and describes snow conditions during the MOSAiC expedition.
A large expert-curated cryo-EM image dataset for machine learning protein particle picking
Authors: Dhakal Ashwin , Gyawali Rajan , Wang Liguo , Cheng Jianlin 
Published: June 2023

AbstractCryo-electron microscopy (cryo-EM) is a powerful technique for determining the structures of biological macromolecular complexes. Picking single-protein particles from cryo-EM micrographs is a crucial step in reconstructing protein structures. However, the widely used template-based particle picking process is labor-intensive and time-consuming. Though machine learning and artificial intelligence (AI) based particle picking can potentially automate the process, its development is hindered by lack of large, high-quality labelled training data. To address this bottleneck, we present CryoPPP, a large, diverse, expert-curated cryo-EM image dataset for protein particle picking and analysis. It consists of labelled cryo-EM micrographs (images) of 34 representative protein datasets selected from the Electron Microscopy Public Image Archive (EMPIAR). The dataset is 2.6 terabytes and includes 9,893 high-resolution micrographs with labelled protein particle coordinates. The labelling process was rigorously validated through 2D particle class validation and 3D density map validation with the gold standard. The dataset is expected to greatly facilitate the development of both AI and classical methods for automated cryo-EM protein particle picking.
A ten-year numerical hindcast of hydrodynamics and sediment dynamics in the Loire Estuary
Authors: Grasso Florent , Caillaud Matthieu 
Published: June 2023

AbstractA numerical hindcast of the macrotidal Loire Estuary (France) has been generated to provide a long-term dataset (2008–2018) of estuarine hydrodynamics, temperature, salinity, and sediment dynamics. This hindcast is based on simulations coupling water motion, wave and mixed sediment models, forced with realistic conditions and extensively validated in the salinity gradient and turbidity maximum areas. These data represent extremely valuable information for diverse scientific communities, providing (i) environmental parameters for ecosystemic studies along the Loire River–sea continuum, (ii) a singular estuarine configuration for inter-comparison of estuarine functioning, and (iii) a ten-year synoptical view of a major estuarine environment of the North Atlantic Ocean.
Long non-coding RNA and circular RNA and coding RNA profiling of plasma exosomes of osteosarcoma by RNA seq
Authors: Liu Yun , Tang Haijun , Li Chong , Huang Nenggan , Miao Jifeng , Chen Lin , Luo Kai , Li Feicui , Liu Shangyu , Liao Shijie , Feng Wenyu , Zhan Xinli , Xie Tianyu , Tang Wei , Wei Qingjun , Lu Jili 
Published: June 2023

AbstractOsteosarcoma (OS) is a primary bone tumor with high malignancy and the mechanism of hematogenous metastasis in OS is still not clear. The plasma exosomes derived from osteosarcoma play a key role in the process of tumor metastasis. Here, we established RNA-seq dataset for lncRNAs, circRNAs and mRNAs in plasma exosomes from 10 OS patients and 5 healthy donors. A total of 329.52 Gb of clean data was obtained. Besides, 1754 lincRNAs, 7096 known and 1935 new circRNA was identified. Finally, gene expression profiles and differentially expressed genes (DEGs) were analyzed among these 15 samples. There were 331 DEGs of mRNA, 132 of lincRNA and 489 of circRNA was obtained, respectively. This data set provides a significant resource for relevant researchers to excavate potential dysregulated lncRNAs, circRNAs and mRNAs of plasma exosomes in OS versus normal conditions.
A phylotranscriptomic dataset of angiosperm species under cold stress
Authors: Wang Shuo , Zhang Yixian , Ye Xiaoxue , Shen Yirong , Liu Hua , Zhao Xijuan , Guo Liangyu , Cao Lu , Du Yunfei , Wu Wenwu 
Published: June 2023

AbstractAngiosperms are one of the most diverse and abundant plant groups that are widely distributed on Earth, from tropical to temperate and polar zones. The wide distribution of angiosperms may be attributed to the evolution of sophisticated mechanisms of environmental adaptability, including cold tolerance. Since the development of high-throughput sequencing, transcriptome has been widely utilized to gain insights into the molecular mechanisms of plants in response to cold stress. However, previous studies generally focused on single or two species, and comparative transcriptome analyses for multispecies responding to cold stress were limited. In this study, we selected 11 representative angiosperm species, performed phylotranscriptome experiments at four time points before and after cold stress, and presented a profile of cold-induced transcriptome changes in angiosperms. Our multispecies cold-responsive RNA-seq datasets provide valuable references for exploring conserved and evolutionary mechanisms of angiosperms in adaptation to cold stress.
A convolutional recurrent neural network with attention for response prediction to repetitive transcranial magnetic stimulation in major depressive disorder
Authors: Shahabi Mohsen Sadat , Shalbaf Ahmad , Rostami Reza , Kazemi Reza 
Published: June 2023

AbstractPrediction of response to Repetitive Transcranial Magnetic Stimulation (rTMS) can build a very effective treatment platform that helps Major Depressive Disorder (MDD) patients to receive timely treatment. We proposed a deep learning model powered up by state-of-the-art methods to classify responders (R) and non-responders (NR) to rTMS treatment. Pre-treatment Electro-Encephalogram (EEG) signal of public TDBRAIN dataset and 46 proprietary MDD subjects were utilized to create time–frequency representations using Continuous Wavelet Transform (CWT) to be fed into the two powerful pre-trained Convolutional Neural Networks (CNN) named VGG16 and EfficientNetB0. Equipping these Transfer Learning (TL) models with Bidirectional Long Short-Term Memory (BLSTM) and attention mechanism for the extraction of most discriminative spatiotemporal features from input images, can lead to superior performance in the prediction of rTMS treatment outcome. Five brain regions named Frontal, Central, Parietal, Temporal, and occipital were assessed and the highest evaluated performance in 46 proprietary MDD subjects was acquired for the Frontal region using the TL-LSTM-Attention model based on EfficientNetB0 with accuracy, sensitivity, specificity, and Area Under the Curve (AUC) of 97.1%, 97.3%, 97.0%, and 0.96 respectively. Additionally, to test the generalizability of the proposed models, these TL-BLSTM-Attention models were evaluated on a public dataset called TDBRAIN and the highest accuracy of 82.3%, the sensitivity of 80.2%, the specificity of 81.9% and the AUC of 0.83 were obtained. Therefore, advanced deep learning methods using a time–frequency representation of EEG signals from the frontal brain region and the convolutional recurrent neural networks equipped with the attention mechanism can construct an accurate platform for the prediction of response to the rTMS treatment.
Enteral administration of the protease inhibitor gabexate mesilate preserves vascular function in experimental trauma/hemorrhagic shock
Authors: J. D. Moreira Nathalia , dos Santos Fernando , Li Joyce B. , Aletti Federico , Irigoyen Maria Claudia C. , Kistler Erik B. 
Published: June 2023

AbstractPreserving vascular function is crucial for preventing multiorgan failure and death in ischemic and low-pressure states such as trauma/hemorrhagic shock (T/HS). It has recently been reported that inhibiting circulating proteases released from the bowel to the circulation during T/HS may preserve vascular function and improve outcomes following T/HS. This study aimed to evaluate the role of the serine protease inhibitor gabexate mesilate (GM) in preserving vascular function during T/HS when given enterally. We studied the vascular reactivity of mesenteric arteries from male Wistar rats treated with enteral GM (10 mg/kg) (GM-treated, n = 6) or control (Shock-control, n = 6) following (T/HS) using pressure myography. Concentration–response curves of endothelial-dependent and endothelial-independent agonists (e.g., acetylcholine, sodium nitroprusside) ranging from 10−10 to 10−5 M were performed. In a second set of experiments, ex-vivo arteries from healthy rats were perfused with plasma from shocked animals from both groups and vascular performance was similarly measured. Arteries from the GM-treated group demonstrated a preserved concentration–response curve to the α1 adrenergic agonist phenylephrine compared to arteries from Shock-control animals (− logEC50: − 5.73 ± 0.25 vs. − 6.48 ± 0.2, Shock-control vs. GM-treated, p = 0.04). When perfused with plasma from GM-treated rats, healthy arteries exhibited an even greater constriction and sensitivity to phenylephrine (− logEC50: − 6.62 ± 0.21 vs. − 7.13 ± 0.21, Shock-control vs. GM-treated, p = 0.02). Enteral GM also preserved the endothelium-dependent vascular response to agonists following T/HS and limited syndecan-1 shedding as a marker of glycocalyx compromise (41.84 ± 9 vs. 17.63 ± 3.97 ng/mL, Shock-control vs. GM-treated, p = 0.02). Syndecan-1 cleavage was correlated with plasma trypsin-like activity (r2 = 0.9611). Enteral gabexate mesilate was able to maintain vascular function in experimental T/HS, which was reflected by improved hemodynamics (mean arterial pressure 50.39 ± 7.91 vs. 64.95 ± 3.43 mmHg, Shock-control vs. GM treated, p = 0.0001). Enteral serine protease inhibition may be a potential therapeutic intervention in the treatment of T/HS.
Physics-informed neural ODE (PINODE): embedding physics into models using collocation points
Authors: Sholokhov Aleksei , Liu Yuying , Mansour Hassan , Nabi Saleh 
Published: June 2023

AbstractBuilding reduced-order models (ROMs) is essential for efficient forecasting and control of complex dynamical systems. Recently, autoencoder-based methods for building such models have gained significant traction, but their demand for data limits their use when the data is scarce and expensive. We propose aiding a model’s training with the knowledge of physics using a collocation-based physics-informed loss term. Our innovation builds on ideas from classical collocation methods of numerical analysis to embed knowledge from a known equation into the latent-space dynamics of a ROM. We show that the addition of our physics-informed loss allows for exceptional data supply strategies that improves the performance of ROMs in data-scarce settings, where training high-quality data-driven models is impossible. Namely, for a problem of modeling a high-dimensional nonlinear PDE, our experiments show$$\times$$×5 performance gains, measured by prediction error, in a low-data regime,$$\times$$×10 performance gains in tasks of high-noise learning,$$\times$$×100 gains in the efficiency of utilizing the latent-space dimension, and$$\times$$×200 gains in tasks of far-out out-of-distribution forecasting relative to purely data-driven models. These improvements pave the way for broader adoption of network-based physics-informed ROMs in compressive sensing and control applications.