bluuebunny/crossref_metadata_2025_split huggingface dataset:
Characterization of SARS-CoV-2 Omicron BA.2.75 clinical isolates
Authors: Uraki Ryuta , Iida Shun , Halfmann Peter J. , Yamayoshi Seiya , Hirata Yuichiro , Iwatsuki-Horimoto Kiyoko , Kiso Maki , Ito Mutsumi , Furusawa Yuri , Ueki Hiroshi , Sakai-Tagawa Yuko , Kuroda Makoto , Maemura Tadashi , Kim Taksoo , Mine Sohtaro , Iwamoto Noriko , Li Rong , Liu Yanan , Larson Deanna , Fukushi Shuetsu , Watanabe Shinji , Maeda Ken , Wang Zhongde , Ohmagari Norio , Theiler James , Fischer Will , Korber Bette , Imai Masaki , Suzuki Tadaki , Kawaoka Yoshihiro 
Published: March 2023

AbstractThe prevalence of the Omicron subvariant BA.2.75 rapidly increased in India and Nepal during the summer of 2022, and spread globally. However, the virological features of BA.2.75 are largely unknown. Here, we evaluated the replicative ability and pathogenicity of BA.2.75 clinical isolates in Syrian hamsters. Although we found no substantial differences in weight change among hamsters infected with BA.2, BA.5, or BA.2.75, the replicative ability of BA.2.75 in the lungs is higher than that of BA.2 and BA.5. Of note, BA.2.75 causes focal viral pneumonia in hamsters, characterized by patchy inflammation interspersed in alveolar regions, which is not observed in BA.5-infected hamsters. Moreover, in competition assays, BA.2.75 replicates better than BA.5 in the lungs of hamsters. These results suggest that BA.2.75 can cause more severe respiratory disease than BA.5 and BA.2 in a hamster model and should be closely monitored.
U.S. West Coast droughts and heat waves exacerbate pollution inequality and can evade emission control policies
Authors: Zeighami Amir , Kern Jordan , Yates Andrew J. , Weber Paige , Bruno August A. 
Published: March 2023

AbstractDroughts reduce hydropower production and heatwaves increase electricity demand, forcing power system operators to rely more on fossil fuel power plants. However, less is known about how droughts and heat waves impact the county level distribution of health damages from power plant emissions. Using California as a case study, we simulate emissions from power plants under a 500-year synthetic weather ensemble. We find that human health damages are highest in hot, dry years. Counties with a majority of people of color and counties with high pollution burden (which are somewhat overlapping) are disproportionately impacted by increased emissions from power plants during droughts and heat waves. Taxing power plant operations based on each plant’s contribution to health damages significantly reduces average exposure. However, emissions taxes do not reduce air pollution damages on the worst polluting days, because supply scarcity (caused by severe heat waves) forces system operators to use every power plant available to avoid causing a blackout.
Directed growth and fusion of membrane-wall microdomains requires CASP-mediated inhibition and displacement of secretory foci
Authors: Barbosa Inês Catarina Ramos , De Bellis Damien , Flückiger Isabelle , Bellani Etienne , Grangé-Guerment Mathieu , Hématy Kian , Geldner Niko 
Published: March 2023

AbstractCasparian strips (CS) are aligned bands of lignin-impregnated cell walls, building an extracellular diffusion barrier in roots. Their structure profoundly differs from tight junctions (TJ), analogous structures in animals. Nonetheless, CS membrane domain (CSD) proteins 1-5 (CASP1-5) are homologues of occludins, TJ components. CASP-marked membranes display cell wall (matrix) adhesion and membrane protein exclusion. A full CASP knock-out now reveals CASPs are not needed for localized lignification, since correctly positioned lignin microdomains still form in the mutant. Ultra-structurally, however, these microdomains are disorganized, showing excessive cell wall growth, lack of exclusion zone and matrix adhesion, and impaired exocyst dynamics. Proximity-labelling identifies a Rab-GTPase subfamily, known exocyst activators, as potential CASP-interactors and demonstrate their localization and function at the CSD. We propose that CASP microdomains displace initial secretory foci by excluding vesicle tethering factors, thereby ensuring rapid fusion of microdomains into a membrane-cell wall band that seals the extracellular space.
Solution-grown BiI/BiI3 van der Waals heterostructures for sensitive X-ray detection
Authors: Zhuang Renzhong , Cai Songhua , Mei Zengxia , Liang Huili , Zhao Ningjiu , Mu Haoran , Yu Wenzhi , Jiang Yan , Yuan Jian , Lau Shuping , Deng Shiming , Han Mingyue , Jin Peng , Wang Cailin , Zhang Guangyu , Lin Shenghuang 
Published: March 2023

AbstractX-ray detectors must be operated at minimal doses to reduce radiation health risks during X-ray security examination or medical inspection, therefore requiring high sensitivity and low detection limits. Although organolead trihalide perovskites have rapidly emerged as promising candidates for X-ray detection due to their low cost and remarkable performance, these materials threaten the safety of the human body and environment due to the presence of lead. Here we present the realization of highly sensitive X-ray detectors based on an environmentally friendly solution-grown thick BiI/BiI3/BiI (BixIy) van der Waals heterostructure. The devices exhibit anisotropic X-ray detection response with a sensitivity up to 4.3 × 104 μC Gy−1 cm−2 and a detection limit as low as 34 nGy s−1. At the same time, our BixIy detectors demonstrate high environmental and hard radiation stabilities. Our work motivates the search for new van der Waals heterostructure classes to realize high-performance X-ray detectors and other optoelectronic devices without employing toxic elements.
Subjective signal strength distinguishes reality from imagination
Authors: Dijkstra Nadine , Fleming Stephen M. 
Published: March 2023

AbstractHumans are voracious imaginers, with internal simulations supporting memory, planning and decision-making. Because the neural mechanisms supporting imagery overlap with those supporting perception, a foundational question is how reality and imagination are kept apart. One possibility is that the intention to imagine is used to identify and discount self-generated signals during imagery. Alternatively, because internally generated signals are generally weaker, sensory strength is used to index reality. Traditional psychology experiments struggle to investigate this issue as subjects can rapidly learn that real stimuli are in play. Here, we combined one-trial-per-participant psychophysics with computational modelling and neuroimaging to show that imagined and perceived signals are in fact intermixed, with judgments of reality being determined by whether this intermixed signal is strong enough to cross a reality threshold. A consequence of this account is that when virtual or imagined signals are strong enough, they become subjectively indistinguishable from reality.
One-pot synthesis of hyperbranched polymers via visible light regulated switchable catalysis
Authors: Zhu Shuaishuai , Zhao Maoji , Zhou Hongru , Wen Yingfeng , Wang Yong , Liao Yonggui , Zhou Xingping , Xie Xiaolin 
Published: March 2023

AbstractSwitchable catalysis promises exceptional efficiency in synthesizing polymers with ever-increasing structural complexity. However, current achievements in such attempts are limited to constructing linear block copolymers. Here we report a visible light regulated switchable catalytic system capable of synthesizing hyperbranched polymers in a one-pot/two-stage procedure with commercial glycidyl acrylate (GA) as a heterofunctional monomer. Using (salen)CoIIICl (1) as the catalyst, the ring-opening reaction under a carbon monoxide atmosphere occurs with high regioselectivity (>99% at the methylene position), providing an alkoxycarbonyl cobalt acrylate intermediate (2a) during the first stage. Upon exposure to light, the reaction enters the second stage, wherein2aserves as a polymerizable initiator for organometallic-mediated radical self-condensing vinyl polymerization (OMR-SCVP). Given the organocobalt chain-end functionality of the resulting hyperbranched poly(glycidyl acrylate) (hb-PGA), a further chain extension process gives access to a core-shell copolymer with brush-on-hyperbranched arm architecture. Notably, the post-modification with 2,2,6,6-tetramethylpiperidine-1-oxyl (TEMPO) affords a metal-freehb-PGA that simultaneously improves the toughness and glass transition temperature of epoxy thermosets, while maintaining their storage modulus.
Pan-cancer classification of single cells in the tumour microenvironment
Authors: Nofech-Mozes Ido , Soave David , Awadalla Philip , Abelson Sagi 
Published: March 2023

AbstractSingle-cell RNA sequencing can reveal valuable insights into cellular heterogeneity within tumour microenvironments (TMEs), paving the way for a deep understanding of cellular mechanisms contributing to cancer. However, high heterogeneity among the same cancer types and low transcriptomic variation in immune cell subsets present challenges for accurate, high-resolution confirmation of cells’ identities. Here we present scATOMIC; a modular annotation tool for malignant and non-malignant cells. We trained scATOMIC on >300,000 cancer, immune, and stromal cells defining a pan-cancer reference across 19 common cancers and employ a hierarchical approach, outperforming current classification methods. We extensively confirm scATOMIC’s accuracy on 225 tumour biopsies encompassing >350,000 cancer and a variety of TME cells. Lastly, we demonstrate scATOMIC’s practical significance to accurately subset breast cancers into clinically relevant subtypes and predict tumours’ primary origin across metastatic cancers. Our approach represents a broadly applicable strategy to analyse multicellular cancer TMEs.
Mid-gap trap state-mediated dark current in organic photodiodes
Authors: Sandberg Oskar J. , Kaiser Christina , Zeiske Stefan , Zarrabi Nasim , Gielen Sam , Maes Wouter , Vandewal Koen , Meredith Paul , Armin Ardalan 
Published: March 2023

AbstractPhotodiodes are ubiquitous in industry and consumer electronics. Constantly emerging new applications for photodiodes demand different mechanical and optoelectronic properties from those provided by conventional inorganic-based semiconductor devices. This has stimulated considerable interest in the use of organic semiconductors, which provide a vast palette of available optoelectronic properties, can be incorporated into flexible form factor geometries, and promise low-cost, low-embodied energy manufacturing from earth-abundant materials. The sensitivity of a photodiode depends critically on the dark current. Organic photodiodes (OPDs), however, are characterized by a much higher dark current than expected for thermally excited radiative transitions. Here we show that the dark saturation current in OPDs is fundamentally limited by mid-gap trap states. This new insight is generated by the universal trend observed for the dark saturation current of a large set of OPDs and further substantiated by sensitive external-quantum-efficiency- and temperature-dependent current measurements. Based on this insight, an upper limit for the specific detectivity is established. A detailed understanding of the origins of noise in any detector is fundamental to defining performance limitations and thus is critical to materials and device selection, and design and optimization for all applications. Our work establishes these important principles for OPDs.
Ascending neurons convey behavioral state to integrative sensory and action selection brain regions
Authors: Chen Chin-Lin , Aymanns Florian , Minegishi Ryo , Matsuda Victor D. V. , Talabot Nicolas , Günel Semih , Dickson Barry J. , Ramdya Pavan 
Published: March 2023

AbstractKnowing one’s own behavioral state has long been theorized as critical for contextualizing dynamic sensory cues and identifying appropriate future behaviors. Ascending neurons (ANs) in the motor system that project to the brain are well positioned to provide such behavioral state signals. However, what ANs encode and where they convey these signals remains largely unknown. Here, through large-scale functional imaging in behaving animals and morphological quantification, we report the behavioral encoding and brain targeting of hundreds of genetically identifiable ANs in the adult fly,Drosophila melanogaster. We reveal that ANs encode behavioral states, specifically conveying self-motion to the anterior ventrolateral protocerebrum, an integrative sensory hub, as well as discrete actions to the gnathal ganglia, a locus for action selection. Additionally, AN projection patterns within the motor system are predictive of their encoding. Thus, ascending populations are well poised to inform distinct brain hubs of self-motion and ongoing behaviors and may provide an important substrate for computations that are required for adaptive behavior.
Assessing the development of mental fatigue during simulated flights with concurrent EEG-fNIRS measurement
Authors: Hamann Anneke , Carstengerdes Nils 
Published: March 2023

AbstractMental fatigue (MF) can impair pilots’ performance and reactions to unforeseen events and is therefore an important concept within aviation. The physiological measurement of MF, especially with EEG and, in recent years, fNIRS, has gained much attention. However, a systematic investigation and comparison of the measurements is seldomly done. We induced MF via time on task during a 90-min simulated flight task and collected concurrent EEG-fNIRS, performance and self-report data from 31 participants. While their subjective MF increased linearly, the participants were able to keep their performance stable over the course of the experiment. EEG data showed an early increase and levelling in parietal alpha power and a slower, but steady increase in frontal theta power. No consistent trend could be observed in the fNIRS data. Thus, more research on fNIRS is needed to understand its possibilities and limits for MF assessment, and a combination with EEG is advisable to compare and validate results. Until then, EEG remains the better choice for continuous MF assessment in cockpit applications because of its high sensitivity to a transition from alert to fatigued, even before performance is impaired.
Phase prediction and experimental realisation of a new high entropy alloy using machine learning
Authors: Singh Swati , Katiyar Nirmal Kumar , Goel Saurav , Joshi Shrikrishna N. 
Published: March 2023

AbstractNearly ~ 108types of High entropy alloys (HEAs) can be developed from about 64 elements in the periodic table. A major challenge for materials scientists and metallurgists at this stage is to predict their crystal structure and, therefore, their mechanical properties to reduce experimental efforts, which are energy and time intensive. Through this paper, we show that it is possible to use machine learning (ML) in this arena for phase prediction to develop novel HEAs. We tested five robust algorithms namely, K-nearest neighbours (KNN), support vector machine (SVM), decision tree classifier (DTC), random forest classifier (RFC) and XGBoost (XGB) in their vanilla form (base models) on a large dataset screened specifically from experimental data concerning HEA fabrication using melting and casting manufacturing methods. This was necessary to avoid the discrepancy inherent with comparing HEAs obtained from different synthesis routes as it causes spurious effects while treating an imbalanced data—an erroneous practice we observed in the reported literature. We found that (i) RFC model predictions were more reliable in contrast to other models and (ii) the synthetic data augmentation is not a neat practice in materials science specially to develop HEAs, where it cannot assure phase information reliably. To substantiate our claim, we compared the vanilla RFC (V-RFC) model for original data (1200 datasets) with SMOTE-Tomek links augmented RFC (ST-RFC) model for the new datasets (1200 original + 192 generated = 1392 datasets). We found that although the ST-RFC model showed a higher average test accuracy of 92%, no significant breakthroughs were observed, when testing the number of correct and incorrect predictions using confusion matrix and ROC-AUC scores for individual phases. Based on our RFC model, we report the development of a new HEA (Ni25Cu18.75Fe25Co25Al6.25) exhibiting an FCC phase proving the robustness of our predictions.
Music that is used while studying and music that is used for sleep share similar musical features, genres and subgroups
Authors: Scarratt Rebecca Jane , Heggli Ole Adrian , Vuust Peter , Sadakata Makiko 
Published: March 2023

AbstractMusic is an integral part of daily human life, and certain types of music are often associated with certain contexts, such as specific music for sleeping or for studying. The mood-arousal hypothesis suggests that music used for studying should be uplifting to boost arousal and increase cognitive performance while previous studies suggest that music used as a sleep aid should be calm, gentle and slow to decrease arousal. In this study, we created the Study music dataset by collecting tracks from Spotify playlists with the words ‘study’ or ‘studying’ in the title or description. In comparison with a pre-existing dataset, the Sleep music dataset, we show that the music’s audio features, as defined by Spotify, are highly similar. Additionally, they share most of the same genres and have similar subgroups after a k-means clustering analysis. We suggest that both sleep music and study music aim to create a pleasant but not too disturbing auditory environment, which enables one to focus on studying and to lower arousal for sleeping. Using large Spotify-based datasets, we were able to uncover similarities between music used in two different contexts one would expect to be different.
Machine learning at the edge for AI-enabled multiplexed pathogen detection
Authors: Ganjalizadeh Vahid , Meena Gopikrishnan G. , Stott Matthew A. , Hawkins Aaron R. , Schmidt Holger 
Published: March 2023

AbstractMultiplexed detection of biomarkers in real-time is crucial for sensitive and accurate diagnosis at the point of use. This scenario poses tremendous challenges for detection and identification of signals of varying shape and quality at the edge of the signal-to-noise limit. Here, we demonstrate a robust target identification scheme that utilizes a Deep Neural Network (DNN) for multiplex detection of single particles and molecular biomarkers. The model combines fast wavelet particle detection with Short-Time Fourier Transform analysis, followed by DNN identification on an AI-specific edge device (Google Coral Dev board). The approach is validated using multi-spot optical excitation of Klebsiella Pneumoniae bacterial nucleic acids flowing through an optofluidic waveguide chip that produces fluorescence signals of varying amplitude, duration, and quality. Amplification-free 3× multiplexing in real-time is demonstrated with excellent specificity, sensitivity, and a classification accuracy of 99.8%. These results show that a minimalistic DNN design optimized for mobile devices provides a robust framework for accurate pathogen detection using compact, low-cost diagnostic devices.
Antiviral activity of marine sulfated glycans against pathogenic human coronaviruses
Authors: Zoepfl Mary , Dwivedi Rohini , Kim Seon Beom , McVoy Michael A. , Pomin Vitor H. 
Published: March 2023

AbstractGreat interest exists towards the discovery and development of broad-spectrum antivirals. This occurs due to the frequent emergence of new viruses which can also eventually lead to pandemics. A reasonable and efficient strategy to develop new broad-spectrum antivirals relies on targeting a common molecular player of various viruses. Heparan sulfate is a sulfated glycosaminoglycan present on the surface of cells which plays a key role as co-receptor in many virus infections. In previous work, marine sulfated glycans (MSGs) were identified as having antiviral activities. Their mechanism of action relies primarily on competitive inhibition of virion binding to heparan sulfate, preventing virus attachment to the cell surface prior to entry. In the current work we used pseudotyped lentivirus particles to investigate in a comparative fashion the inhibitory properties of five structurally defined MSGs against SARS-CoV-1, SARS-CoV-2, MERS-CoV, and influenza A virus (IAV). MSGs include the disaccharide-repeating sulfated galactan from the red alga Botryocladia occidentalis, the tetrasaccharide-repeating sulfated fucans from the sea urchin Lytechinus variegatus and from the sea cucumber Isostichopus badionotus, and the two marine fucosylated chondroitin sulfates from the sea cucumbers I. badionotus and Pentacta pygmaea. Results indicate specificity of action against SARS-CoV-1 and SARS-CoV-2. Curiously, the MSGs showed decreased inhibitory potencies against MERS-CoV and negligible action against IAV. Among the five MSGs, the two sulfated fucans here studied deserve further attention since they have the lowest anticoagulant effects but still present potent and selective antiviral properties.
Development and clinical validation of Swaasa AI platform for screening and prioritization of pulmonary TB
Authors: Yellapu Gayatri Devi , Rudraraju Gowrisree , Sripada Narayana Rao , Mamidgi Baswaraj , Jalukuru Charan , Firmal Priyanka , Yechuri Venkat , Varanasi Sowmya , Peddireddi Venkata Sudhakar , Bhimarasetty Devi Madhavi , Kanisetti Sidharth , Joshi Niranjan , Mohapatra Prasant , Pamarthi Kiran 
Published: March 2023

AbstractAcoustic signal analysis has been employed in various medical devices. However, studies involving cough sound analysis to screen the potential pulmonary tuberculosis (PTB) suspects are very few. The main objective of this cross-sectional validation study was to develop and validate the Swaasa AI platform to screen and prioritize at risk patients for PTB based on the signature cough sound as well as symptomatic information provided by the subjects. The voluntary cough sound data was collected at Andhra Medical College-India. An Algorithm based on multimodal convolutional neural network architecture and feedforward artificial neural network (tabular features) was built and validated on a total of 567 subjects, comprising 278 positive and 289 negative PTB cases. The output from these two models was combined to detect the likely presence (positive cases) of PTB. In the clinical validation phase, the AI-model was found to be 86.82% accurate in detecting the likely presence of PTB with 90.36% sensitivity and 84.67% specificity. The pilot testing of model was conducted at a peripheral health care centre, RHC Simhachalam-India on 65 presumptive PTB cases. Out of which, 15 subjects truly turned out to be PTB positive with a positive predictive value of 75%. The validation results obtained from the model are quite encouraging. This platform has the potential to fulfil the unmet need of a cost-effective PTB screening method. It works remotely, presents instantaneous results, and does not require a highly trained operator. Therefore, it could be implemented in various inaccessible, resource-poor parts of the world.