bluuebunny/crossref_metadata_2025_split huggingface dataset:
DNA strand breaks and gaps target retroviral intasome binding and integration
Authors: Senavirathne Gayan , London James , Gardner Anne , Fishel Richard , Yoder Kristine E. 
Published: November 2023

AbstractRetrovirus integration into a host genome is essential for productive infections. The integration strand transfer reaction is catalyzed by a nucleoprotein complex (Intasome) containing the viral integrase (IN) and the reverse transcribed (RT) copy DNA (cDNA). Previous studies suggested that DNA target-site recognition limits intasome integration. Using single molecule Förster resonance energy transfer (smFRET), we show prototype foamy virus (PFV) intasomes specifically bind to DNA strand breaks and gaps. These break and gap DNA discontinuities mimic oxidative base excision repair (BER) lesion-processing intermediates that have been shown to affect retrovirus integration in vivo. The increased DNA binding events targeted strand transfer to the break/gap site without inducing substantial intasome conformational changes. The major oxidative BER substrate 8-oxo-guanine as well as a G/T mismatch or +T nucleotide insertion that typically introduce a bend or localized flexibility into the DNA, did not increase intasome binding or targeted integration. These results identify DNA breaks or gaps as modulators of dynamic intasome-target DNA interactions that encourage site-directed integration.
Rapid threat assessment in the Drosophila thermosensory system
Authors: Jouandet Genevieve C. , Alpert Michael H. , Simões José Miguel , Suhendra Richard , Frank Dominic D. , Levy Joshua I. , Para Alessia , Kath William L. , Gallio Marco 
Published: November 2023

AbstractNeurons that participate in sensory processing often display “ON” responses, i.e., fire transiently at the onset of a stimulus. ON transients are widespread, perhaps universal to sensory coding, yet their function is not always well-understood. Here, we show that ON responses in the Drosophila thermosensory system extrapolate the trajectory of temperature change, priming escape behavior if unsafe thermal conditions are imminent. First, we show that second-order thermosensory projection neurons (TPN-IIIs) and their Lateral Horn targets (TLHONs), display ON responses to thermal stimuli, independent of direction of change (heating or cooling) and of absolute temperature. Instead, they track the rate of temperature change, with TLHONs firing exclusively to rapid changes (>0.2 °C/s). Next, we use connectomics to track TLHONs’ output to descending neurons that control walking and escape, and modeling and genetic silencing to demonstrate how ON transients can flexibly amplify aversive responses to small thermal change. Our results suggest that, across sensory systems, ON transients may represent a general mechanism to systematically anticipate and respond to salient or dangerous conditions.
Exhaustive search for optimal molecular geometries using imaginary-time evolution on a quantum computer
Authors: Kosugi Taichi , Nishi Hirofumi , Matsushita Yu-ichiro 
Published: November 2023

AbstractThis study proposes a nonvariational scheme for geometry optimization of molecules for the first-quantized eigensolver, which is a recently proposed framework for quantum chemistry using probabilistic imaginary-time evolution (PITE). In this scheme, the nuclei in a molecule are treated as classical point charges while the electrons are treated as quantum mechanical particles. The electronic states and candidate geometries are encoded as a superposition of many-qubit states, for which a histogram created from repeated measurements gives the global minimum of the energy surface. We demonstrate that the circuit depth per step scales as$${{{\mathcal{O}}}}({n}_{{\rm {e}}}^{2}{{{\rm{poly}}}}(\log {n}_{{\rm {e}}}))$$O(ne2poly(logne))for the electron numberne, which can be reduced to$${{{\mathcal{O}}}}({n}_{{\rm {e}}}{{{\rm{poly}}}}(\log {n}_{{\rm {e}}}))$$O(nepoly(logne))if extra$${{{\mathcal{O}}}}({n}_{{\rm {e}}}\log {n}_{{\rm {e}}})$$O(nelogne)qubits are available. Moreover, resource estimation implies that the total computational time of our scheme starting from a good initial guess may exhibit overall quantum advantage in molecule size and candidate number. The proposed scheme is corroborated using numerical simulations. Additionally, a scheme adapted to variational calculations is examined that prioritizes saving circuit depths for noisy intermediate-scale quantum (NISQ) devices. A classical system composed only of charged particles is considered as a special case of the scheme. The new efficient scheme will assist in achieving scalability in practical quantum chemistry on quantum computers.
Error per single-qubit gate below 10−4 in a superconducting qubit
Authors: Li Zhiyuan , Liu Pei , Zhao Peng , Mi Zhenyu , Xu Huikai , Liang Xuehui , Su Tang , Sun Weijie , Xue Guangming , Zhang Jing-Ning , Liu Weiyang , Jin Yirong , Yu Haifeng 
Published: November 2023

AbstractImplementing arbitrary single-qubit gates with near perfect fidelity is among the most fundamental requirements in gate-based quantum information processing. In this work, we fabricate a transmon qubit with long coherence times and demonstrate single-qubit gates with the average gate error below 10−4, i.e. (7.42 ± 0.04) × 10−5 by randomized benchmarking (RB). To understand the error sources, we experimentally obtain an error budget, consisting of the decoherence errors lower bounded by (4.62 ± 0.04) × 10−5 and the leakage rate per gate of (1.16 ± 0.04) × 10−5. Moreover, we reconstruct the process matrices for the single-qubit gates by the gate set tomography (GST), with which we simulate RB sequences and obtain single-qubit fidelities consistent with experimental results. We also observe non-Markovian behavior in the experiment of long-sequence GST, which may provide guidance for further calibration. The demonstration extends the upper limit that the average fidelity of single-qubit gates can reach in a transmon-qubit system, and thus can be an essential step towards practical and reliable quantum computation in the near future.
Scalable and high throughput photothermal water disinfection with negligible CO2 footprint utilizing nanostructured carbon coatings
Authors: Sah Ananya , Mandal Atindra Kanti , Tiwari Shubham , Mukherji Soumyo , Subramaniam Chandramouli 
Published: November 2023

AbstractWater heating and disinfection with reduced energy and CO2 footprint demands new and efficient materials for solar-thermal conversion technologies. Here, we demonstrate nanostructured porous hard-carbon florets (NCF) as effective solar absorber coating achieving excellent photon thermalization efficiency (87%). Functional NCF coating on three-dimensionally tapered helical solar receivers generate high surface temperatures (up to 95 °C). Such ‘green-heat’ is channeled to heat water up to 82 °C that simultaneously results in water disinfection through thermal shock. Untreated lake-water with high turbidity (5 NTU), high bacterial load (106 CFU mL−1) and pathogenic fungi is effectively disinfected in a continuous flow process. Translating this, a fully automated SWAP prototype (solar water antimicrobial purifier), delivers bacteria free hot water at an output capacity of 42 L m−2 day−1 with the lowest CO2 footprint (5 kg L−1) in comparison to all other existing approaches (>40 kg L−1).
CreelCat, a Catalog of United States Inland Creel and Angler Survey Data
Authors: Sievert Nicholas A. , Lynch Abigail J. , Embke Holly S. , Robertson Ashley , Lang Mitchel , Kaz Anna L. , Robertson Matthew D. , Midway Stephen R. , Wszola Lyndsie , Paukert Craig P. 
Published: November 2023

AbstractThe United States Inland Creel and Angler Survey Catalog (CreelCat) contains a national compilation of angler and creel survey data collected by natural resource management agencies across the United States (including Washington, D.C. and Puerto Rico). These surveys are used to help inform the management of recreational fisheries, by collecting information about anglers including what they are catching and harvesting, the amount of effort they expend, their angling preferences, and demographic information. As of May 1, 2023, CreelCat houses over 14,729 surveys from 33 states, Puerto Rico, and Washington, D.C., comprising 235 data fields across 8 tables. These tables contain 235,015 records of fish catch and harvest metrics, 27,250 angler preference metrics, 14,729 records of survey characteristics, 13,576 records of effort metrics, and 409 records of angler demographics. Though individual creel surveys are often deployed to meet local science and management objectives, creel data aggregated across jurisdictions has the potential to address larger scale research and management needs.
Early and fair COVID-19 outcome risk assessment using robust feature selection
Authors: Giuste Felipe O. , He Lawrence , Lais Peter , Shi Wenqi , Zhu Yuanda , Hornback Andrew , Tsai Chiche , Isgut Monica , Anderson Blake , Wang May D. 
Published: November 2023

AbstractPersonalized medicine plays an important role in treatment optimization for COVID-19 patient management. Early treatment in patients at high risk of severe complications is vital to prevent death and ventilator use. Predicting COVID-19 clinical outcomes using machine learning may provide a fast and data-driven solution for optimizing patient care by estimating the need for early treatment. In addition, it is essential to accurately predict risk across demographic groups, particularly those underrepresented in existing models. Unfortunately, there is a lack of studies demonstrating the equitable performance of machine learning models across patient demographics. To overcome this existing limitation, we generate a robust machine learning model to predict patient-specific risk of death or ventilator use in COVID-19 positive patients using features available at the time of diagnosis. We establish the value of our solution across patient demographics, including gender and race. In addition, we improve clinical trust in our automated predictions by generating interpretable patient clustering, patient-level clinical feature importance, and global clinical feature importance within our large real-world COVID-19 positive patient dataset. We achieved 89.38% area under receiver operating curve (AUROC) performance for severe outcomes prediction and our robust feature ranking approach identified the presence of dementia as a key indicator for worse patient outcomes. We also demonstrated that our deep-learning clustering approach outperforms traditional clustering in separating patients by severity of outcome based on mutual information performance. Finally, we developed an application for automated and fair patient risk assessment with minimal manual data entry using existing data exchange standards.
How intra-source imbalanced datasets impact the performance of deep learning for COVID-19 diagnosis using chest X-ray images
Authors: Zhang Zhang , Zhang Xiaoyong , Ichiji Kei , Bukovský Ivo , Homma Noriyasu 
Published: November 2023

AbstractOver the past decade, the use of deep learning has been widely increasing in the medical image diagnosis field. Deep learning-based methods’ (DLMs) performance strongly relies on training data. Therefore, researchers often focus on collecting as much data as possible from different medical facilities or developing approaches to avoid the impact of inter-category imbalance (ICI), which means a difference in data quantity among categories. However, due to the ICI within each medical facility, medical data are often isolated and acquired in different settings among medical facilities, known as the issue of intra-source imbalance (ISI) characteristic. This imbalance also impacts the performance of DLMs but receives negligible attention. In this study, we study the impact of the ISI on DLMs by comparison of the version of a deep learning model that was trained separately by an intra-source imbalanced chest X-ray (CXR) dataset and an intra-source balanced CXR dataset for COVID-19 diagnosis. The finding is that using the intra-source imbalanced dataset causes a serious training bias, although the dataset has a good inter-category balance. In contrast, the deep learning model performed a reliable diagnosis when trained on the intra-source balanced dataset. Therefore, our study reports clear evidence that the intra-source balance is vital for training data to minimize the risk of poor performance of DLMs.
Small signal modulation of photonic crystal surface emitting lasers
Authors: Orchard Jonathan R. , Ivanov Pavlo , McKenzie Adam F. , Hill Calum H. , Javed Ibrahim , Munro Connor W. , Kettle Jeff , Hogg Richard A. , Childs David T. D. , Taylor Richard J. E. 
Published: November 2023

AbstractWe report the small-signal characterization of a PCSEL device, extracting damping factors and modulation efficiencies, and demonstrating -3 dB modulation bandwidths of up to 4.26 GHz. Based on modelling we show that, by reducing the device width and improving the active region design for high-speed modulation, direct modulation frequencies in excess of 50 GHz are achievable.
Characterization of nano-silica vegetable grease with diffusing wave spectroscopy DWS and Raman spectroscopy
Authors: Drabik Jolanta , Kozdrach Rafal , Szczerek Marian 
Published: November 2023

AbstractThe diffusing wave spectroscopy (DWS) method made it possible to identify changes in the dynamics of the free movement of particles in the grease under the influence of temperature, which changed the viscoelastic properties of the grease. Changes in the parameters determined by DWS method influenced changes in the chemical structure, which was confirmed by Raman spectroscopy, determining the integral intensity of the unsaturated to saturated bond bands found in the grease. The article presents the results of the influence of temperature on changes in the viscoelastic states of vegetable grease evaluated on the basis of properties determined by DWS (diffusing wave spectroscopy). The following parameters were used to evaluate the viscoelastic states: the intensity correlation function (ICF), the correlation function of mean square displacement (MSD), the elastic modulus G′ and the viscosity modulus G″. A significant effect of temperature on changes in the microstructure of vegetable grease was observed, which was reflected in the viscoelastic parameters. The dynamics of the free movement of molecules in the grease was changed, which affected the elasticity of the system and the displacement of the G′ and G″ modules towards higher frequencies.
Distribution of sika deer (Cervus nippon) and the bioclimatic impact on their habitats in South Korea
Authors: Dhakal Thakur , Kim Tae-Su , Kim Seong-Hyeon , Tiwari Shraddha , Kim Jun-Young , Jang Gab-Sue , Lee Do-Hun 
Published: November 2023

AbstractInvasive species and climate change are primary factors influencing biodiversity, and examining the behavior of invasive species is essential for effective conservation management. Here, we report the global distribution of the sika deer (Cervus nippon) based on locations reported in published literature (Google Scholar), the Global Biodiversity Information Facility (GBIF) database, and the International Union for Conservation of Nature report. We used the maximum entropy (Maxent) model to examine the impact of climate change on sika deer habitats in South Korea based on GBIF occurrence data and WorldClim bioclimatic variables. Habitat suitability analysis was performed using the Maxent model under Representative Concentration Pathways (RCPs) 4.5 and 8.5 (for predicted climatic conditions in both 2050 and 2070) to project the effects of different climate change scenarios on South Korean sika deer habitats. We identified that the sika deer is distributed in 39 countries worldwide. Due to climate change effects, South Korean sika deer habitats will decline by approximately 24.98% and 20.63% (under RCP 4.5) and by 50.51% and 57.35% (under RCP 8.5) by 2050 and 2070, respectively. Our findings shed light on sika deer ecology and provide reference data for future conservation management strategies and policy design.
DNA damage repair-related gene signature for identifying the immune status and predicting the prognosis of hepatocellular carcinoma
Authors: Lu Yongpan , Wang Sen , Chi Tingting , Zhao Yuli , Guo Huimin , Wang Haizheng , Feng Li 
Published: November 2023

AbstractThe heterogeneity of hepatocellular carcinoma (HCC) poses a challenge for accurate prognosis prediction. DNA damage repair genes (DDRGs) have an impact on a wide range of malignancies. However, the relevance of these genes in HCC prognosis has received little attention. In this study, we aimed to develop a prognostic signature to identify novel therapy options for HCC. We acquired mRNA expression profiles and clinical data for HCC patients from The Cancer Genome Atlas (TCGA) database. A polygenic prognostic model for HCC was constructed using selection operator Cox analysis and least absolute shrinkage. The model was validated using International Cancer Genome Consortium (ICGC) data. Overall survival (OS) between the high-risk and low-risk groups was compared using Kaplan‒Meier analysis. Independent predictors of OS were identified through both univariate and multivariate Cox analyses. To determine immune cell infiltration scores and activity in immune-related pathways, a single-sample gene set enrichment analysis was performed. The protein and mRNA expression levels of the prognostic genes between HCC and normal liver tissues were also examined by immunohistochemistry (IHC), immunofluorescence (IF) and quantitative real-time PCR (qRT-PCR). A novel ten-gene signature (CHD1L, HDAC1, KPNA2, MUTYH, PPP2R5B, NEIL3, POLR2L, RAD54B, RUVBL1 and SPP1) was established for HCC prognosis prediction. Patients in the high-risk group had worse OS than those in the low-risk group. Receiver operating characteristic curve analysis confirmed the predictive ability of this prognostic gene signature. Multivariate Cox analysis showed that the risk score was an independent predictor of OS. Functional analysis revealed a strong association with cell cycle and antigen binding pathways, and the risk score was highly correlated with tumor grade, tumor stage, and types of immune infiltrate. High expression levels of the prognostic genes were significantly correlated with increased sensitivity of cancer cells to antitumor drugs. IHC, IF and qRT-PCR all indicated that the prognostic genes were highly expressed in HCC relative to normal liver tissue, consistent with the results of bioinformatics analysis. Ten DDRGs were utilized to create a new signature for identifying the immunological state of HCC and predicting prognosis. In addition, blocking these genes could represent a promising treatment.
Williams–Beuren syndrome shapes the gut microbiota metaproteome
Authors: Marzano Valeria , Levi Mortera Stefano , Vernocchi Pamela , Del Chierico Federica , Marangelo Chiara , Guarrasi Valerio , Gardini Simone , Dentici Maria Lisa , Capolino Rossella , Digilio Maria Cristina , Di Donato Maddalena , Spasari Iolanda , Abreu Maria Teresa , Dallapiccola Bruno , Putignani Lorenza 
Published: November 2023

AbstractWilliams–Beuren syndrome (WBS) is a rare genetic neurodevelopmental disorder with multi-systemic manifestations. The evidence that most subjects with WBS face gastrointestinal (GI) comorbidities, have prompted us to carry out a metaproteomic investigation of their gut microbiota (GM) profile compared to age-matched healthy subjects (CTRLs). Metaproteomic analysis was carried out on fecal samples collected from 41 individuals with WBS, and compared with samples from 45 CTRLs. Stool were extracted for high yield in bacterial protein group (PG) content, trypsin-digested and analysed by nanoLiquid Chromatography-Mass Spectrometry. Label free quantification, taxonomic assignment by the lowest common ancestor (LCA) algorithm and functional annotations by COG and KEGG databases were performed. Data were statistically interpreted by multivariate and univariate analyses. A WBS GM functional dissimilarity respect to CTRLs, regardless age distribution, was reported. The alterations in function of WBSs GM was primarily based on bacterial pathways linked to carbohydrate transport and metabolism and energy production. Influence of diet, obesity, and GI symptoms was assessed, highlighting changes in GM biochemical patterns, according to WBS subsets’ stratification. The LCA-derived ecology unveiled WBS-related functionally active bacterial signatures: Bacteroidetes related to over-expressed PGs, and Firmicutes, specifically the specie Faecalibacterium prausnitzii, linked to under-expressed PGs, suggesting a depletion of beneficial bacteria. These new evidences on WBS gut dysbiosis may offer novel targets for tailored interventions.
Wolbachia dominance influences the Culex quinquefasciatus microbiota
Authors: Flores Guillermo A. M. , Lopez Rocio P. , Cerrudo Carolina S. , Perotti M. Alejandra , Consolo V. Fabiana , Berón Corina M. 
Published: November 2023

AbstractMicroorganisms present in mosquitoes and their interactions are key factors affecting insect development. Among them, Wolbachia is closely associated with the host and affects several fitness parameters. In this study, the bacterial and fungal microbiota from two laboratory Culex quinquefasciatus isolines (wild type and tetracycline-cured) were characterized by metagenome amplicon sequencing of the ITS2 and 16S rRNA genes at different developmental stages and feeding conditions. We identified 572 bacterial and 61 fungal OTUs. Both isolines presented variable bacterial communities and different trends in the distribution of diversity among the groups. The lowest bacterial richness was detected in sugar-fed adults of the cured isoline, whereas fungal richness was highly reduced in blood-fed mosquitoes. Beta diversity analysis indicated that isolines are an important factor in the differentiation of mosquito bacterial communities. Considering composition, Penicillium was the dominant fungal genus, whereas Wolbachia dominance was inversely related to that of Enterobacteria (mainly Thorsellia and Serratia). This study provides a more complete overview of the mosquito microbiome, emphasizing specific highly abundant components that should be considered in microorganism manipulation approaches to control vector-borne diseases.
Wavelet scattering transform application in classification of retinal abnormalities using OCT images
Authors: Baharlouei Zahra , Rabbani Hossein , Plonka Gerlind 
Published: November 2023

AbstractTo assist ophthalmologists in diagnosing retinal abnormalities, Computer Aided Diagnosis has played a significant role. In this paper, a particular Convolutional Neural Network based on Wavelet Scattering Transform (WST) is used to detect one to four retinal abnormalities from Optical Coherence Tomography (OCT) images. Predefined wavelet filters in this network decrease the computation complexity and processing time compared to deep learning methods. We use two layers of the WST network to obtain a direct and efficient model. WST generates a sparse representation of the images which is translation-invariant and stable concerning local deformations. Next, a Principal Component Analysis classifies the extracted features. We evaluate the model using four publicly available datasets to have a comprehensive comparison with the literature. The accuracies of classifying the OCT images of the OCTID dataset into two and five classes were$$100\%$$100%and$$82.5\%$$82.5%, respectively. We achieved an accuracy of$$96.6\%$$96.6%in detecting Diabetic Macular Edema from Normal ones using the TOPCON device-based dataset. Heidelberg and Duke datasets contain DME, Age-related Macular Degeneration, and Normal classes, in which we achieved accuracy of$$97.1\%$$97.1%and$$94.4\%$$94.4%, respectively. A comparison of our results with the state-of-the-art models shows that our model outperforms these models for some assessments or achieves nearly the best results reported so far while having a much smaller computational complexity.