bluuebunny/crossref_metadata_2025_split huggingface dataset:
Restoring speech intelligibility for hearing aid users with deep learning
Authors: Diehl Peter Udo , Singer Yosef , Zilly Hannes , Schönfeld Uwe , Meyer-Rachner Paul , Berry Mark , Sprekeler Henning , Sprengel Elias , Pudszuhn Annett , Hofmann Veit M. 
Published: February 2023

AbstractAlmost half a billion people world-wide suffer from disabling hearing loss. While hearing aids can partially compensate for this, a large proportion of users struggle to understand speech in situations with background noise. Here, we present a deep learning-based algorithm that selectively suppresses noise while maintaining speech signals. The algorithm restores speech intelligibility for hearing aid users to the level of control subjects with normal hearing. It consists of a deep network that is trained on a large custom database of noisy speech signals and is further optimized by a neural architecture search, using a novel deep learning-based metric for speech intelligibility. The network achieves state-of-the-art denoising on a range of human-graded assessments, generalizes across different noise categories and—in contrast to classic beamforming approaches—operates on a single microphone. The system runs in real time on a laptop, suggesting that large-scale deployment on hearing aid chips could be achieved within a few years. Deep learning-based denoising therefore holds the potential to improve the quality of life of millions of hearing impaired people soon.
Determination of optimum dose based of biological responses of lethal dose (LD25, 50, 75) and growth reduction (GR25, 50, 75) in ‘Yaghouti’ grape due to gamma radiation
Authors: Ghasemi-Soloklui Ali Akbar , Kordrostami Mojtaba , Karimi Rouhollah 
Published: February 2023

AbstractIonizing radiations are a helpful technique and have improved financial potential in developing new and unique commercially important fruit tree varieties. The ‘Yaghouti’ grape cuttings were treated with 0 (control), 10, 20, 30, 40, 50, 60, 70, 80, 90, and 100 Gy gamma ray (γ) doses. The objectives of this study were to (1) investigate the effects of γ radiation on the survival rate, leaves, shoots, and root morphometric traits after γ irradiation; (2) Measurement of the 25, 50, and 75% lethal dose and 25, 50, and 75% growth reduction dose based on leaves, shoots, and root morphometric characteristics to estimate grape radiosensitivity; (3) Ultimately, determining the optimum dose of γ irradiation based biological responses (LD25, 50, 75 and GR25, 50, 75) in Yaghouti grape. The findings demonstrate that the lethal dose of the ‘Yaghouti’ was 18 Gy for LD25, 30 Gy for LD50, and 48 Gy for LD75, respectively. Furthermore, our findings showed that increasing the γ dose had a harmed ngative effect on vine growth, as evidenced by a decrease in plant height, root number, root volume, leaf area, aerial biomass, root biomass, and internode number of the ‘Yaghouti’ grape plants. Our results showed that between the aerial parts of vines, the leaf area and aerial biomass had higher radiosensitivity than plant height and other aerial parts of the plants based on data from GR25, GR50, and GR75. Moreover, GR studies of root characteristics revealed that root number and biomass root had higher radiation sensitivity than root volume. According to biological responses (LD25, 50, 75 and GR25, 50, 75) in the ‘Yaghouti’ grape, 30 Gy of γ radiation is the optimum dose for preliminary mutagenesis investigations.
Classification of emotional stress and physical stress using a multispectral based deep feature extraction model
Authors: Hong Kan 
Published: February 2023

AbstractA classification model (Stress Classification-Net) of emotional stress and physical stress is proposed, which can extract classification features based on multispectral and tissue blood oxygen saturation (StO2) characteristics. Related features are extracted on this basis, and the learning model with frequency domain and signal amplification is proposed for the first time. Given that multispectral imaging signals are time series data, time series StO2is extracted from spectral signals. The proper region of interest (ROI) is obtained by a composite criterion, and the ROI source is determined by the universality and robustness of the signal. The frequency-domain signals of ROI are further obtained by wavelet transform. To fully utilize the frequency-domain characteristics, the multi-neighbor vector of locally aggregated descriptors (MN-VLAD) model is proposed to extract useful features. The acquired time series features are finally put into the long short-term memory (LSTM) model to learn the classification characteristics. Through SC-NET model, the classification signals of emotional stress and physical stress are successfully obtained. Experiments show that the classification result is encouraging, and the accuracy of the proposed algorithm is over 90%.
Three-dimensional simulations of embolic stroke and an equation for sizing emboli from imaging
Authors: Hague James P. , Keelan Jonathan , Beishon Lucy , Swienton David , Robinson Thompson G. , Chung Emma M. L. 
Published: February 2023

AbstractStroke simulations are needed to run in-silico trials, develop hypotheses for clinical studies and to interpret ultrasound monitoring and radiological imaging. We describe proof-of-concept three-dimensional stroke simulations, carrying out in silico trials to relate lesion volume to embolus diameter and calculate probabilistic lesion overlap maps, building on our previous Monte Carlo method. Simulated emboli were released into an in silico vasculature to simulate 1000 s of strokes. Infarct volume distributions and probabilistic lesion overlap maps were determined. Computer-generated lesions were assessed by clinicians and compared with radiological images. The key result of this study is development of a three-dimensional simulation for embolic stroke and its application to an in silico clinical trial. Probabilistic lesion overlap maps showed that the lesions from small emboli are homogeneously distributed throughout the cerebral vasculature. Mid-sized emboli were preferentially found in posterior cerebral artery (PCA) and posterior region of the middle cerebral artery (MCA) territories. For large emboli, MCA, PCA and anterior cerebral artery (ACA) lesions were comparable to clinical observations, with MCA, PCA then ACA territories identified as the most to least probable regions for lesions to occur. A power law relationship between lesion volume and embolus diameter was found. In conclusion, this article showed proof-of-concept for large in silico trials of embolic stroke including 3D information, identifying that embolus diameter could be determined from infarct volume and that embolus size is critically important to the resting place of emboli. We anticipate this work will form the basis of clinical applications including intraoperative monitoring, determining stroke origins, and in silico trials for complex situations such as multiple embolisation.
The importance of batch sensitization in missing value imputation
Authors: Hui Harvard Wai Hann , Kong Weijia , Peng Hui , Goh Wilson Wen Bin 
Published: February 2023

AbstractData analysis is complex due to a myriad of technical problems. Amongst these, missing values and batch effects are endemic. Although many methods have been developed for missing value imputation (MVI) and batch correction respectively, no study has directly considered the confounding impact of MVI on downstream batch correction. This is surprising as missing values are imputed during early pre-processing while batch effects are mitigated during late pre-processing, prior to functional analysis. Unless actively managed, MVI approaches generally ignore the batch covariate, with unknown consequences. We examine this problem by modelling three simple imputation strategies: global (M1), self-batch (M2) and cross-batch (M3) first via simulations, and then corroborated on real proteomics and genomics data. We report that explicit consideration of batch covariates (M2) is important for good outcomes, resulting in enhanced batch correction and lower statistical errors. However, M1 and M3 are error-generating: global and cross-batch averaging may result in batch-effect dilution, with concomitant and irreversible increase in intra-sample noise. This noise is unremovable via batch correction algorithms and produces false positives and negatives. Hence, careless imputation in the presence of non-negligible covariates such as batch effects should be avoided.
Bandgap engineering approach for designing CuO/Mn3O4/CeO2 heterojunction as a novel photocatalyst for AOP-assisted degradation of Malachite green dye
Authors: Gupta Shaswat Vikram , Kulkarni Vihangraj Vijaykumar , Ahmaruzzaman Md. 
Published: February 2023

AbstractA ternary nanohybrid CuO/Mn3O4/CeO2 was developed in the present work using a co-precipitation-assisted hydrothermal method. The designed photocatalyst's structural, morphology, elemental composition, electronic states of elements, and optical properties were studied using corresponding analytical techniques. Results from PXRD, TEM/HRTEM, XPS, EDAX, and PL showed that the desired nanostructure had formed. Using Tauc's energy band gap plot, it was determined that the nanostructures band gap was ~ 2.44 eV, which showed the band margins of the various moieties, CeO2, Mn3O4, and CuO, had modified. Thus, improved redox conditions led to a substantial decrease in the recombination rate of electron–hole pairs, which was further explained by a PL study in that charge separation plays a key role. Under exposure to visible light irradiation for 60 min, it was revealed that the photocatalyst achieved 98.98% of photodegradation efficiency for malachite green (MG) dye. The process of photodegradation proceeded according to a pseudo-first-order reaction kinetic model with an excellent rate of reaction of 0.07295 min−1 with R2 = 0.99144. The impacts of different reaction variables, inorganic salts, and water matrices were investigated. This research seeks to create a ternary nanohybrid photocatalyst with high photostability, visible spectrum activity, and reusability up to four cycles.
High thermoelectric performance of flexible nanocomposite films based on Bi2Te3 nanoplates and carbon nanotubes selected using ultracentrifugation
Authors: Chiba Tomoyuki , Yabuki Hayato , Takashiri Masayuki 
Published: February 2023

AbstractThermoelectric generators with flexibility and high performance near 300 K have the potential to be employed in self-supporting power supplies for Internet of Things (IoT) devices. Bismuth telluride (Bi2Te3) exhibits high thermoelectric performance, and single-walled carbon nanotubes (SWCNTs) show excellent flexibility. Therefore, composites of Bi2Te3 and SWCNTs should exhibit an optimal structure and high performance. In this study, flexible nanocomposite films based on Bi2Te3 nanoplates and SWCNTs were prepared by drop casting on a flexible sheet, followed by thermal annealing. Bi2Te3 nanoplates were synthesized using the solvothermal method, and SWCNTs were synthesized using the super-growth method. To improve the thermoelectric properties of the SWCNTs, ultracentrifugation with a surfactant was performed to selectively obtain suitable SWCNTs. This process selects thin and long SWCNTs but does not consider the crystallinity, chirality distribution, and diameters. A film consisting of Bi2Te3 nanoplates and the thin and long SWCNTs exhibited high electrical conductivity, which was six times higher than that of a film with SWCNTs obtained without ultracentrifugation; this is because the SWCNTs uniformly connected the surrounding nanoplates. The power factor was 6.3 μW/(cm K2), revealing that this is one of the best-performing flexible nanocomposite films. The findings of this study can support the application of flexible nanocomposite films in thermoelectric generators to provide self-supporting power supplies for IoT devices.
3D single shot lensless incoherent optical imaging using coded phase aperture system with point response of scattered airy beams
Authors: Kumar Ravi , Anand Vijayakumar , Rosen Joseph 
Published: February 2023

AbstractInterferenceless coded aperture correlation holography (I-COACH) techniques have revolutionized the field of incoherent imaging, offering multidimensional imaging capabilities with a high temporal resolution in a simple optical configuration and at a low cost. The I-COACH method uses phase modulators (PMs) between the object and the image sensor, which encode the 3D location information of a point into a unique spatial intensity distribution. The system usually requires a one-time calibration procedure in which the point spread functions (PSFs) at different depths and/or wavelengths are recorded. When an object is recorded under identical conditions as the PSF, the multidimensional image of the object is reconstructed by processing the object intensity with the PSFs. In the previous versions of I-COACH, the PM mapped every object point to a scattered intensity distribution or random dot array pattern. The scattered intensity distribution results in a low SNR compared to a direct imaging system due to optical power dilution. Due to the limited focal depth, the dot pattern reduces the imaging resolution beyond the depth of focus if further multiplexing of phase masks is not performed. In this study, I-COACH has been realized using a PM that maps every object point into a sparse random array of Airy beams. Airy beams during propagation exhibit a relatively high focal depth with sharp intensity maxima that shift laterally following a curved path in 3D space. Therefore, sparse, randomly distributed diverse Airy beams exhibit random shifts with respect to one another during propagation, generating unique intensity distributions at different distances while retaining optical power concentrations in small areas on the detector. The phase-only mask displayed on the modulator was designed by random phase multiplexing of Airy beam generators. The simulation and experimental results obtained for the proposed method are significantly better in SNR than in the previous versions of I-COACH.
Systematic analysis identifies REST as an oncogenic and immunological biomarker in glioma
Authors: Wang Guan , Yang Xiaxin , Qi Mei , Li Meng , Dong Meng , Xu Rui , Zhang Chen 
Published: February 2023

AbstractThe repressor element 1 silencing transcription factor (REST) has been proposed to function as a transcription factor to silence gene transcription by binding to repressor element 1 (RE1), a highly conserved DNA motif. The functions of REST in various tumors have been studied, but its role and correlation with immune cell infiltration remains uncertain in gliomas. REST expression was analyzed in datasets of The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) and validated by the Gene Expression Omnibus and Human Protein Atlas databases. The clinical prognosis of REST was evaluated by clinical survival data of TCGA cohort and validated by Chinese Glioma Genome Atlas cohort. MicroRNAs (miRNAs) contributing to REST overexpression in glioma were identified by a combination of a series of in silico analyses, including expression analysis, correlation analysis, and survival analysis. The correlations between immune cell infiltration level and REST expression were analyzed by TIMER2 and GEPIA2 tools. Enrichment analysis of REST was performed using STRING and Metascape tools. The expression and function of predicted upstream miRNAs at REST and their association with glioma malignancy and migration were also confirmed in glioma cell lines. REST was highly expressed and associated with poorer overall survival and disease-specific survival in glioma and some other tumors. MiR-105-5p and miR-9-5p were identified as the most potential upstream miRNAs of REST in glioma patient cohort and experiments in vitro. REST expression was positively correlated with infiltration of immune cells and the expression of immune checkpoints such as PD1/PD-L1 and CTLA-4 in glioma. Furthermore, histone deacetylase 1 (HDAC1) was a potential REST-related gene in glioma. Enrichment analysis of REST found chromatin organization and histone modification were the most significant enriched terms, and Hedgehog-Gli pathway might be involved in the effect of REST on the pathogenesis of glioma. Our study suggests REST to be an oncogenic gene and the biomarker of poor prognosis in glioma. High REST expression might affect the tumor microenvironment of glioma. More basic experiments and large clinical trials aimed at the carcinogenetic study of REST in glioma will be needed in the future.
Cooperation of N- and C-terminal substrate transmembrane domain segments in intramembrane proteolysis by γ-secretase
Authors: Werner Nadine T. , Högel Philipp , Güner Gökhan , Stelzer Walter , Wozny Manfred , Aßfalg Marlene , Lichtenthaler Stefan F. , Steiner Harald , Langosch Dieter 
Published: February 2023

AbstractIntramembrane proteases play a pivotal role in biology and medicine, but how these proteases decode cleavability of a substrate transmembrane (TM) domain remains unclear. Here, we study the role of conformational flexibility of a TM domain, as determined by deuterium/hydrogen exchange, on substrate cleavability by γ-secretase in vitro andin cellulo. By comparing hybrid TMDs based on the natural amyloid precursor protein TM domain and an artificial poly-Leu non-substrate, we find that substrate cleavage requires conformational flexibility within the N-terminal half of the TMD helix (TM-N). Robust cleavability also requires the C-terminal TM sequence (TM-C) containing substrate cleavage sites. Since flexibility of TM-C does not correlate with cleavage efficiency, the role of the TM-C may be defined mainly by its ability to form a cleavage-competent state near the active site, together with parts of presenilin, the enzymatic component of γ-secretase. In sum, cleavability of a γ-secretase substrate appears to depend on cooperating TM domain segments, which deepens our mechanistic understanding of intramembrane proteolysis.
Uncovering hidden network architecture from spiking activities using an exact statistical input-output relation of neurons
Authors: Shomali Safura Rashid , Rasuli Seyyed Nader , Ahmadabadi Majid Nili , Shimazaki Hideaki 
Published: February 2023

AbstractIdentifying network architecture from observed neural activities is crucial in neuroscience studies. A key requirement is knowledge of the statistical input-output relation of single neurons in vivo. By utilizing an exact analytical solution of the spike-timing for leaky integrate-and-fire neurons under noisy inputs balanced near the threshold, we construct a framework that links synaptic type, strength, and spiking nonlinearity with the statistics of neuronal population activity. The framework explains structured pairwise and higher-order interactions of neurons receiving common inputs under different architectures. We compared the theoretical predictions with the activity of monkey and mouse V1 neurons and found that excitatory inputs given to pairs explained the observed sparse activity characterized by strong negative triple-wise interactions, thereby ruling out the alternative explanation by shared inhibition. Moreover, we showed that the strong interactions are a signature of excitatory rather than inhibitory inputs whenever the spontaneous rate is low. We present a guide map of neural interactions that help researchers to specify the hidden neuronal motifs underlying observed interactions found in empirical data.
Selectivity for food in human ventral visual cortex
Authors: Jain Nidhi , Wang Aria , Henderson Margaret M. , Lin Ruogu , Prince Jacob S. , Tarr Michael J. , Wehbe Leila 
Published: February 2023

AbstractVisual cortex contains regions of selectivity for domains of ecological importance. Food is an evolutionarily critical category whose visual heterogeneity may make the identification of selectivity more challenging. We investigate neural responsiveness to food using natural images combined with large-scale human fMRI. Leveraging the improved sensitivity of modern designs and statistical analyses, we identify two food-selective regions in the ventral visual cortex. Our results are robust across 8 subjects from the Natural Scenes Dataset (NSD), multiple independent image sets and multiple analysis methods. We then test our findings of food selectivity in an fMRI “localizer” using grayscale food images. These independent results confirm the existence of food selectivity in ventral visual cortex and help illuminate why earlier studies may have failed to do so. Our identification of food-selective regions stands alongside prior findings of functional selectivity and adds to our understanding of the organization of knowledge within the human visual system.
Piezoelectric response of disordered lead-based relaxor ferroelectrics
Authors: Rojac Tadej 
Published: February 2023

AbstractLead-based relaxor ferroelectric perovskite oxides, exemplified by the (1–x)Pb(Mg1/3Nb2/3)O3–xPbTiO3 (PMN-PT) solid solution, are a group of multifunctional materials with unique dielectric and piezoelectric properties. The long-lasting question that has intrigued the research community for decades is whether and to which extent the disordered relaxor nature of these materials is implicated in the exceedingly large piezoelectric response observed in both single crystals and ceramics. In this Perspective, focusing on PMN-PT, I briefly review and discuss the current understanding of the polar structure of relaxor ferroelectrics, followed by its possible implications in the large piezoelectricity. A critical review of the existing data confirms a significant progress made in recent years while opening up new questions related to the structure-property relations in these complex materials.
Functional two-dimensional high-entropy materials
Authors: Nemani Srinivasa Kartik , Torkamanzadeh Mohammad , Wyatt Brian C. , Presser Volker , Anasori Babak 
Published: February 2023

AbstractMultiple principal element or high-entropy materials have recently been studied in the two-dimensional (2D) materials phase space. These promising classes of materials combine the unique behavior of solid-solution and entropy-stabilized systems with high aspect ratios and atomically thin characteristics of 2D materials. The current experimental space of these materials includes 2D transition metal oxides, carbides/carbonitrides/nitrides (MXenes), dichalcogenides, and hydrotalcites. However, high-entropy 2D materials have the potential to expand into other types, such as 2D metal-organic frameworks, 2D transition metal carbo-chalcogenides, and 2D transition metal borides (MBenes). Here, we discuss the entropy stabilization from bulk to 2D systems, the effects of disordered multi-valent elements on lattice distortion and local electronic structures and elucidate how these local changes influence the catalytic and electrochemical behavior of these 2D high-entropy materials. We also provide a perspective on 2D high-entropy materials research and its challenges and discuss the importance of this emerging field of nanomaterials in designing tunable compositions with unique electronic structures for energy, catalytic, electronic, and structural applications.