bluuebunny/crossref_metadata_2025_split huggingface dataset:
Machine learning and topological data analysis identify unique features of human papillae in 3D scans
Authors: Andreeva Rayna , Sarkar Anwesha , Sarkar Rik 
Published: December 2023

AbstractThe tongue surface houses a range of papillae that are integral to the mechanics and chemistry of taste and textural sensation. Although gustatory function of papillae is well investigated, the uniqueness of papillae within and across individuals remains elusive. Here, we present the first machine learning framework on 3D microscopic scans of human papillae ($$n=2092$$

n
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2092

), uncovering the uniqueness of geometric and topological features of papillae. The finer differences in shapes of papillae are investigated computationally based on a number of features derived from discrete differential geometry and computational topology. Interpretable machine learning techniques show that persistent homology features of the papillae shape are the most effective in predicting the biological variables. Models trained on these features with small volumes of data samples predict the type of papillae with an accuracy of 85%. The papillae type classification models can map the spatial arrangement of filiform and fungiform papillae on a surface. Remarkably, the papillae are found to be distinctive across individuals and an individual can be identified with an accuracy of 48% among the 15 participants from a single papillae. Collectively, this is the first evidence demonstrating that tongue papillae can serve as a unique identifier, and inspires a new research direction for food preferences and oral diagnostics.
The longitudinal biochemical profiling of TBI in a drop weight model of TBI
Authors: Yilmaz Ali , Liraz-Zaltsman Sigal , Shohami Esther , Gordevičius Juozas , Kerševičiūtė Ieva , Sherman Eric , Bahado-Singh Ray O. , Graham Stewart F. 
Published: December 2023

AbstractTraumatic brain injury (TBI) is a major cause of mortality and disability worldwide, particularly among individuals under the age of 45. It is a complex, and heterogeneous disease with a multifaceted pathophysiology that remains to be elucidated. Metabolomics has the potential to identify metabolic pathways and unique biochemical profiles associated with TBI. Herein, we employed a longitudinal metabolomics approach to study TBI in a weight drop mouse model to reveal metabolic changes associated with TBI pathogenesis, severity, and secondary injury. Using proton nuclear magnetic resonance (1H NMR) spectroscopy, we biochemically profiled post-mortem brain from mice that suffered mild TBI (N = 25; 13 male and 12 female), severe TBI (N = 24; 11 male and 13 female) and sham controls (N = 16; 11 male and 5 female) at baseline, day 1 and day 7 following the injury. 1H NMR-based metabolomics, in combination with bioinformatic analyses, highlights a few significant metabolites associated with TBI severity and perturbed metabolism related to the injury. We report that the concentrations of taurine, creatinine, adenine, dimethylamine, histidine, N-Acetyl aspartate, and glucose 1-phosphate are all associated with TBI severity. Longitudinal metabolic observation of brain tissue revealed that mild TBI and severe TBI lead distinct metabolic profile changes. A multi-class model was able to classify the severity of injury as well as time after TBI with estimated 86% accuracy. Further, we identified a high degree of correlation between respective hemisphere metabolic profiles (r > 0.84, p < 0.05, Pearson correlation). This study highlights the metabolic changes associated with underlying TBI severity and secondary injury. While comprehensive, future studies should investigate whether: (a) the biochemical pathways highlighted here are recapitulated in the brain of TBI sufferers and (b) if the panel of biomarkers are also as effective in less invasively harvested biomatrices, for objective and rapid identification of TBI severity and prognosis.
Real-life use of tolvaptan in ADPKD: a retrospective analysis of a large Canadian cohort
Authors: Calvaruso Luca , Yau Kevin , Akbari Pedram , Nasri Fatemah , Khowaja Saima , Wang Bill , Haghighi Amirreza , Khalili Korosh , Pei York 
Published: December 2023

AbstractTolvaptan is the first disease-modifying drug proven to slow eGFR decline in high-risk patients with ADPKD. However, barriers from the patient perspective to its use in real-life settings have not been systemically examined in a large cohort. This was a single-center, retrospective study of 523 existing or new patients with ADPKD followed at the Center for Innovative Management of PKD in Toronto, Ontario, between January 1, 2016 to December 31, 2018. All patients underwent clinical assessment including total kidney volume measurements and Mayo Clinic Imaging Class (MCIC). Those who were deemed to be at high risk were offered tolvaptan with their preference (yes or no) and reasons for their choices recorded. Overall, 315/523 (60%) patients had MCIC 1C-1E; however, only 96 (30%) of them were treated with tolvaptan at their last follow-up. Among these high-risk patients, those not treated versus treated with tolvaptan were more likely to have a higher eGFR (82 ± 26 vs. 61 ± 27 ml/min/1.73 m2), CKD stages 1–2 (79% vs. 41%), and MCIC 1C (63% vs. 31%). The most common reasons provided for not taking tolvaptan were lifestyle preference related to the aquaretic effect (51%), older age ≥ 60 (12%), and pregnancy/family planning (6%). In this real-world experience, at least 60% of patients with ADPKD considered to be at high risk for progression to ESKD by imaging were not treated with tolvaptan; most of them had early stages of CKD with well-preserved eGFR and as such, were prime targets for tolvaptan therapy to slow disease progression. Given that the most common reason for tolvaptan refusal was the concern for intolerability of the aquaretic side-effect, strategies to mitigate this may help to reduce this barrier to tolvaptan therapy.
Leveraging a KRAS-based signature to predict the prognosis and drug sensitivity of colon cancer and identifying SPINK4 as a new biomarker
Authors: Huo Jian-Ting , Tuersun Abudumaimaitijiang , Yu Su-Yue , Zhang Yu-Chen , Feng Wen-Qing , Xu Zhuo-Qing , Zhao Jing-Kun , Zong Ya-Ping , Lu Ai-Guo 
Published: December 2023

AbstractKRAS is one of the leading mutations reported in colon cancer. However, there are few studies on the application of KRAS related signature in predicting prognosis and drug sensitivity of colon cancer patient. We identified KRAS related differentially expressed genes (DEGs) using The Cancer Genome Atlas (TCGA) database. A signature closely related to overall survival was recognized with Kaplan–Meier survival analysis and univariate cox regression analysis. Then we validated this signature with overall expression score (OE score) algorithm using both scRNA-seq and bulk RNA-seq data. Based on this signature, we performed LASSO cox regression to establish a prognostic model, and corresponding scores were calculated. Differences in genomic alteration, immune microenvironment, drug sensitivity between high- and low-KRD score groups were investigated. A KRAS related signature composed of 80 DEGs in colon cancer were recognized, among which 19 genes were selected to construct a prognostic model. This KRAS related signature was significantly correlated with worse prognosis. Furthermore, patients who scored lower in the prognostic model presented a higher likelihood of responding to chemotherapy, targeted therapy and immunotherapy. Furthermore, among the 19 selected genes in the model, SPINK4 was identified as an independent prognostic biomarker. Further validation in vitro indicated the knockdown of SPINK4 promoted the proliferation and migration of SW48 cells. In conclusion, a novel KRAS related signature was identified and validated based on clinical and genomic information from TCGA and GEO databases. The signature was proved to regulate genomic alteration, immune microenvironment and drug sensitivity in colon cancer, and thus might serve as a predictor for individual prognosis and treatment.
A fine pore-preserved deep neural network for porosity analytics of a high burnup U-10Zr metallic fuel
Authors: Wang Haotian , Xu Fei , Cai Lu , Salvato Daniele , Di Lemma Fidelma Giulia , Capriotti Luca , Yao Tiankai , Xian Min 
Published: December 2023

AbstractU-10 wt.% Zr (U-10Zr) metallic fuel is the leading candidate for next-generation sodium-cooled fast reactors. Porosity is one of the most important factors that impacts the performance of U-10Zr metallic fuel. The pores generated by the fission gas accumulation can lead to changes in thermal conductivity, fuel swelling, Fuel-Cladding Chemical Interaction (FCCI) and Fuel-Cladding Mechanical Interaction (FCMI). Therefore, it is crucial to accurately segment and analyze porosity to understand the U-10Zr fuel system to design future fast reactors. To address the above issues, we introduce a workflow to process and analyze multi-source Scanning Electron Microscope (SEM) image data. Moreover, an encoder-decoder-based, deep fully convolutional network is proposed to segment pores accurately by integrating the residual unit and the densely-connected units. Two SEM 250 × field of view image datasets with different formats are utilized to evaluate the new proposed model’s performance. Sufficient comparison results demonstrate that our method quantitatively outperforms two popular deep fully convolutional networks. Furthermore, we conducted experiments on the third SEM 2500 × field of view image dataset, and the transfer learning results show the potential capability to transfer the knowledge from low-magnification images to high-magnification images. Finally, we use a pre-trained network to predict the pores of SEM images in the whole cross-sectional image and obtain quantitative porosity analysis. Our findings will guide the SEM microscopy data collection efficiently, provide a mechanistic understanding of the U-10Zr fuel system and bridge the gap between advanced characterization to fuel system design.
PACT - Prediction of amyloid cross-interaction by threading
Authors: Wojciechowski Jakub W. , Szczurek Witold , Szulc Natalia , Szefczyk Monika , Kotulska Malgorzata 
Published: December 2023

AbstractAmyloid proteins are often associated with the onset of diseases, including Alzheimer’s, Parkinson’s and many others. However, there is a wide class of functional amyloids that are involved in physiological functions, e.g., formation of microbial biofilms or storage of hormones. Recent studies showed that an amyloid fibril could affect the aggregation of another protein, even from a different species. This may result in amplification or attenuation of the aggregation process. Insight into amyloid cross-interactions may be crucial for better understanding of amyloid diseases and the potential influence of microbial amyloids on human proteins. However, due to the demanding nature of the needed experiments, knowledge of such interactions is still limited. Here, we present PACT (Prediction of Amyloid Cross-interaction by Threading) - the computational method for the prediction of amyloid cross-interactions. The method is based on modeling of a heterogeneous fibril formed by two amyloidogenic peptides. The resulting structure is assessed by the structural statistical potential that approximates its plausibility and energetic stability. PACT was developed and first evaluated mostly on data collected in the AmyloGraph database of interacting amyloids and achieved high values of Area Under ROC (AUC=0.88) and F1 (0.82). Then, we applied our method to study the interactions of CsgA - a bacterial biofilm protein that was not used in our in-reference datasets, which is expressed in several bacterial species that inhabit the human intestines - with two human proteins. The study included alpha-synuclein, a human protein that is involved in Parkinson’s disease, and human islet amyloid polypeptide (hIAPP), which is involved in type 2 diabetes. In both cases, PACT predicted the appearance of cross-interactions. Importantly, the method indicated specific regions of the proteins, which were shown to play a central role in both interactions. We experimentally confirmed the novel results of the indicated CsgA fragments interacting with hIAPP based on the kinetic characteristics obtained with the ThT assay. PACT opens the possibility of high-throughput studies of amyloid interactions. Importantly, it can work with fairly long protein fragments, and as a purely physicochemical approach, it relies very little on scarce training data. The tool is available as a web server at https://pact.e-science.pl/pact/. The local version can be downloaded from https://github.com/KubaWojciechowski/PACT.
Non-destructive and efficient method for obtaining miRNA information in cells by artificial extracellular vesicles
Authors: Maeda Fumio , Adachi Shungo , Natsume Tohru 
Published: December 2023

AbstractIn recent years, research has explored the use of microRNA (miRNA) analysis in extracellular vesicles (EVs) as a minimally invasive strategy for the diagnosis and prediction of diseases. This is because miRNAs in EVs partly reflect the miRNA information and cellular status of the origin cells. However, not all intracellular miRNAs are internalized into EVs. Therefore, the miRNA information obtained from EVs is limited. To get more miRNA information, we aimed to produce artificial EVs (aEVs) encapsulating Argonaute 2 (Ago2) miRNA-binding protein, which actively incorporate miRNAs within themselves. In this study, we utilized the protein EPN-01, which is capable of releasing aEVs encapsulating it and associated proteins. This system enables us to obtain more miRNA species and increase each miRNA’s yield in the EV fraction. Furthermore, we examined whether miRNAs in the EV fraction using our system reflect the cellular condition. In cells treated with CoCl2, a reagent for inducing a hypoxia-mimic state, we detected a change in the level of hypoxia marker miR-210 with aEVs. To the best of our knowledge, this is the first report on a method to increase the yield and variety of endogenous miRNAs in the EV fraction. This approach leads to improved accuracy of cell status assessment using miRNAs in EVs.
Tiny stresses are capable of triggering earthquakes and tremors in Arunachal Himalaya
Authors: Saini Tony , Bansal Abhey Ram , Rao N. Purnachandra , Pasricha Rajat , Vempati Venkatesh 
Published: December 2023

AbstractThe Arunachal Himalaya has been hosting some notable events in the recent past. The tectonic history of Arunachal Himalaya is complex and has been influenced by several major tectonic events, including the 1950 Mw8.6 Assam-Tibet earthquake. In this study, we explored the effect of dynamic stresses generated by teleseismic events on the triggering of seismicity in the region. We analyzed 34 large teleseismic events since 2010 and found triggering during six events. The change in seismicity was also confirmed by analysis with the STA/LTA method. The triggering in the region occurred in the form of earthquakes and tremors. The dynamic stress as low as 1 kPa was found capable of triggering. The back-azimuth angle does not play an important role in the triggering. The angle direction of incoming waves with respect to the fault ~ 60° and ~ 120° is the possible reason for triggering in the region. The triggering occurred in the Mishmi and Main Central Thrust regions. The largest triggered event, ML2.3, was triggered 7.5 h after the 2012 Indian Ocean earthquake of Mw8.6. The region is tectonically very sensitive and tiny stresses are capable of triggering seismicity in Arunachal Pradesh.
Tree size diversity is the major driver of aboveground carbon storage in dryland agroforestry parklands
Authors: Noulèkoun Florent , Mensah Sylvanus , Kim HyungSub , Jo Heejae , Gouwakinnou Gérard N. , Houéhanou Thierry D. , Mensah Michael , Naab Jesse , Son Yowhan , Khamzina Asia 
Published: December 2023

AbstractDespite the importance of agroforestry parkland systems for ecosystem and livelihood benefits, evidence on determinants of carbon storage in parklands remains scarce. Here, we assessed the direct and indirect influence of human management (selective harvesting of trees), abiotic factors (climate, topography, and soil) and multiple attributes of species diversity (taxonomic, functional, and structural) on aboveground carbon (AGC) stocks in 51 parklands in drylands of Benin. We used linear mixed-effects regressions and structural equation modeling to test the relative effects of these predictors on AGC stocks. We found that structural diversity (tree size diversity, HDBH) had the strongest (effect size β = 0.59, R2 = 54%) relationship with AGC stocks, followed by community-weighted mean of maximum height (CWMMAXH). Taxonomic diversity had no significant direct relationship with AGC stocks but influenced the latter indirectly through its negative effect on CWMMAXH, reflecting the impact of species selection by farmers. Elevation and soil total organic carbon content positively influenced AGC stocks both directly and indirectly via HDBH. No significant association was found between AGC stocks and tree harvesting factor. Our results suggest the mass ratio, niche complementarity and environmental favorability as underlying mechanisms of AGC storage in the parklands. Our findings also highlight the potential role of human-driven filtering of local species pool in regulating the effect of biodiversity on AGC storage in the parklands. We conclude that the promotion of AGC stocks in parklands is dependent on protecting tree regeneration in addition to enhancing tree size diversity and managing tall-stature trees.
A novel post-weld treatment using nanostructured metallic multilayer for superior fatigue strength
Authors: Brunow Jakob , Spalek Niclas , Mohammadi Fawad , Rutner Marcus 
Published: December 2023

AbstractWelded joints exhibit fatigue failure potential from weld geometry and characteristics of the heat affected zone. In order to counteract fatigue, structures and components require larger thicknesses resulting in heavier designs exhausting the finite natural resources. We hereby introduce a novel post-weld treatment, which postpones or even prevents fatigue failure of the welded connection. A Cu/Ni nanostructured metallic multilayer (NMM) is applied via electrodeposition and a 300–600% increase in usable lifetime compared to the untreated weld is observed. A FAT class 190 with a slope of k = 6 is proposed for the design of NMM treated butt welds. Material mechanisms responsible for the fatigue strength increase are introduced herein. A case study shows that the design of offshore wind turbine support structures applying NMM post-weld treatment enables a lifetime extension as well as a 28% weight reduction compared to the structure without post-weld treatment.
Highly dynamic inflammatory and excitability transcriptional profiles in hippocampal CA1 following status epilepticus
Authors: Galvis-Montes Daniel S. , van Loo Karen M. J. , van Waardenberg Ashley J. , Surges Rainer , Schoch Susanne , Becker Albert J. , Pitsch Julika 
Published: December 2023

AbstractTransient brain insults including status epilepticus (SE) can initiate a process termed ‘epileptogenesis’ that results in chronic temporal lobe epilepsy. As a consequence, the entire tri-synaptic circuit of the hippocampus is fundamentally impaired. A key role in epileptogenesis has been attributed to the CA1 region as the last relay station in the hippocampal circuit and as site of aberrant plasticity, e.g. mediated by acquired channelopathies. The transcriptional profiles of the distinct hippocampal neurons are highly dynamic during epileptogenesis. Here, we aimed to elucidate the early SE-elicited mRNA signature changes and the respective upstream regulatory cascades in CA1. RNA sequencing of CA1 was performed in the mouse pilocarpine-induced SE model at multiple time points ranging from 6 to 72 h after the initial insult. Bioinformatics was used to decipher altered gene expression, signalling cascades and their corresponding cell type profiles. Robust transcriptomic changes were detected at 6 h after SE and at subsequent time points during early epileptogenesis. Major differentially expressed mRNAs encoded primarily immediate early and excitability-related gene products, as well as genes encoding immune signalling factors. Binding sites for the transcription factors Nfkb1, Spi1, Irf8, and two Runx family members, were enriched within promoters of differentially expressed genes related to major inflammatory processes, whereas the transcriptional repressors Suz12, Nfe2l2 and Rest were associated with hyperexcitability and GABA / glutamate receptor activity. CA1 quickly responds to SE by inducing transcription of genes linked to inflammation and excitation stress. Transcription factors mediating this transcriptomic switch represent targets for new highly selected, cell type and time window-specific anti-epileptogenic strategies.
Superconducting transition edge bolometer for high-flux neutron detection
Authors: Brock Mette Bybjerg , Østergaard Emil Visby , Busi Matteo , Wulff Anders C. , Abrahamsen Asger Bech , Kuhn Luise Theil 
Published: December 2023

AbstractNeeds for neutron detection and monitoring in high neutron flux environments are increasing in several different fields. A completely solid-state, current mode bolometric detector is constructed as a solid substrate transition edge sensor based on a high-T$$_c$$csuperconducting meander. The detector consists of four individual pixels of which three pixels include$${^{10}{\hbox {B}}_{4}\hbox {C}}$$10B4Cneutron absorption layers. The absorbed energy per neutron absorption reaction is modelled and compared to experimental data. The response of the tested detector is directly correlated to a cold neutron beam with a flux of$${1.8\times 10^{8}}\,{\hbox {n}/{\hbox {cm}}^2/\hbox {s}}$$1.8×108n/cm2/smodulated by a slit. The signal is found to be an order of magnitude higher than the thermal background. The dynamics described by the temporal saturation constants is governed by a modulation frequency less than$${1}\hbox { Hz}$$1Hz. The thermal response is dynamic and never fully saturates for$${50}\hbox { s}$$50sexposures. The efficiency for this proof-of-principle design is 1–2%. Possibilities for optimization are identified, that will increase the efficiency to become comparable to existing solid boron-10 detectors. The existing detectors with event-based read-out have limited functionality in high flux environments. The superconducting bolometer described in this work using current-mode readout will pave the way for high flux applications.