bluuebunny/crossref_metadata_2025_split huggingface dataset:
Single cell dual-omic atlas of the human developing retina
Authors: Zuo Zhen , Cheng Xuesen , Ferdous Salma , Shao Jianming , Li Jin , Bao Yourong , Li Jean , Lu Jiaxiong , Jacobo Lopez Antonio , Wohlschlegel Juliette , Prieve Aric , Thomas Mervyn G. , Reh Thomas A. , Li Yumei , Moshiri Ala , Chen Rui 
Published: August 2024

AbstractThe development of the retina is under tight temporal and spatial control. To gain insights into the molecular basis of this process, we generate a single-nuclei dual-omic atlas of the human developing retina with approximately 220,000 nuclei from 14 human embryos and fetuses aged between 8 and 23-weeks post-conception with matched macular and peripheral tissues. This atlas captures all major cell classes in the retina, along with a large proportion of progenitors and cell-type-specific precursors. Cell trajectory analysis reveals a transition from continuous progression in early progenitors to a hierarchical development during the later stages of cell type specification. Both known and unrecorded candidate transcription factors, along with gene regulatory networks that drive the transitions of various cell fates, are identified. Comparisons between the macular and peripheral retinae indicate a largely consistent yet distinct developmental pattern. This atlas offers unparalleled resolution into the transcriptional and chromatin accessibility landscapes during development, providing an invaluable resource for deeper insights into retinal development and associated diseases.
Two-dimensional electrons at mirror and twistronic twin boundaries in van der Waals ferroelectrics
Authors: McHugh James G. , Li Xue , Soltero Isaac , Fal’ko Vladimir I. 
Published: August 2024

AbstractSemiconducting transition metal dichalcogenides (MX2) occur in 2H and rhombohedral (3R) polytypes, respectively distinguished by anti-parallel and parallel orientation of consecutive monolayer lattices. In its bulk form, 3R-MX2 is ferroelectric, hosting an out-of-plane electric polarisation, the direction of which is dictated by stacking. Here, we predict that twin boundaries, separating adjacent polarisation domains with reversed built-in electric fields, are able to host two-dimensional electrons and holes with an areal density reaching ~ 1013cm−2. Our modelling suggests that n-doped twin boundaries have a more promising binding energy than p-doped ones, whereas hole accumulation is stable at external surfaces of a twinned film. We also propose that assembling pairs of mono-twin films with a ‘magic’ twist angle θ* that provides commensurability between the moiré pattern at the interface and the accumulated carrier density, should promote a regime of strongly correlated states of electrons, such as Wigner crystals, and we specify the values of θ* for homo- and heterostructures of various TMDs.
Continuous tuning of persistent luminescence wavelength by intermediate-phase engineering in inorganic crystals
Authors: Zhang Xin , Suo Hao , Guo Yang , Chen Jiangkun , Wang Yu , Wei Xiaohe , Zheng Weilin , Li Shuohan , Wang Feng 
Published: August 2024

AbstractMulticolor tuning of persistent luminescence has been extensively studied by deliberately integrating various luminescent units, known as activators or chromophores, into certain host compounds. However, it remains a formidable challenge to fine-tune the persistent luminescence spectra either in organic materials, such as small molecules, polymers, metal-organic complexes and carbon dots, or in doped inorganic crystals. Herein, we present a strategy to delicately control the persistent luminescence wavelength by engineering sub-bandgap donor-acceptor states in a series of single-phase Ca(Sr)ZnOS crystals. The persistent luminescence emission peak can be quasi-linearly tuned across a broad wavelength range (500–630 nm) as a function of Sr/Ca ratio, achieving a precision down to ~5 nm. Theoretical calculations reveal that the persistent luminescence wavelength fine-tuning stems from constantly lowered donor levels accompanying the modified band structure by Sr alloying. Besides, our experimental results show that these crystals exhibit a high initial luminance of 5.36 cd m−2 at 5 sec after charging and a maximum persistent luminescence duration of 6 h. The superior, color-tunable persistent luminescence enables a rapid, programable patterning technique for high-throughput optical encryption.
Clinical symptoms and neuroanatomical substrates of daytime sleepiness in Parkinson’s disease
Authors: Rosinvil Thaïna , Postuma Ronald B. , Rahayel Shady , Bellavance Amélie , Daneault Véronique , Montplaisir Jacques , Lina Jean-Marc , Carrier Julie , Gagnon Jean-François 
Published: August 2024

AbstractClinical and neuroanatomical correlates of daytime sleepiness in Parkinson’s disease (PD) remain inconsistent in the literature. Two studies were conducted here. The first evaluated the interrelation between non-motor and motor symptoms, using a principal component analysis, associated with daytime sleepiness in PD. The second identified the neuroanatomical substrates associated with daytime sleepiness in PD using magnetic resonance imaging (MRI). In the first study, 77 participants with PD completed an extensive clinical, cognitive testing and a polysomnographic recording. In the second study, 29 PD participants also underwent MRI acquisition of T1-weighted images. Vertex-based cortical and subcortical surface analysis, deformation-based morphometry, and voxel-based morphometry were performed to assess the association between daytime sleepiness severity and structural brain changes in participants. In both studies, the severity of daytime sleepiness and the presence of excessive daytime sleepiness (EDS; total score >10) were measured using the Epworth Sleepiness Scale. We found that individuals with EDS had a higher score on a component including higher dosage of dopamine receptor agonists, motor symptoms severity, shorter sleep latency, and greater sleep efficiency. Moreover, increased daytime sleepiness severity was associated with a larger surface area in the right insula, contracted surfaces in the right putamen and right lateral amygdala, and a larger surface in the right posterior amygdala. Hence, daytime sleepiness in PD was associated with dopaminergic receptor agonists dosage, motor impairment, and objective sleep measures. Moreover, neuroanatomical changes in cortical and subcortical regions related to vigilance, motor, and emotional states were associated with more severe daytime sleepiness.
Comparison of beta peak detection algorithms for data-driven deep brain stimulation programming strategies in Parkinson’s disease
Authors: Baker Sunderland K. , Radcliffe Erin M. , Kramer Daniel R. , Ojemann Steven , Case Michelle , Zarns Caleb , Holt-Becker Abbey , Raike Robert S. , Baumgartner Alexander J. , Kern Drew S. , Thompson John A. 
Published: August 2024

AbstractOscillatory activity within the beta frequency range (13–30 Hz) serves as a Parkinson’s disease biomarker for tailoring deep brain stimulation (DBS) treatments. Currently, identifying clinically relevant beta signals, specifically frequencies of peak amplitudes within the beta spectral band, is a subjective process. To inform potential strategies for objective clinical decision making, we assessed algorithms for identifying beta peaks and devised a standardized approach for both research and clinical applications. Employing a novel monopolar referencing strategy, we utilized a brain sensing device to measure beta peak power across distinct contacts along each DBS electrode implanted in the subthalamic nucleus. We then evaluated the accuracy of ten beta peak detection algorithms against a benchmark established by expert consensus. The most accurate algorithms, all sharing similar underlying algebraic dynamic peak amplitude thresholding approaches, matched the expert consensus in performance and reliably predicted the clinical stimulation parameters during follow-up visits. These findings highlight the potential of algorithmic solutions to overcome the subjective bias in beta peak identification, presenting viable options for standardizing this process. Such advancements could lead to significant improvements in the efficiency and accuracy of patient-specific DBS therapy parameterization.
DynaMight: estimating molecular motions with improved reconstruction from cryo-EM images
Authors: Schwab Johannes , Kimanius Dari , Burt Alister , Dendooven Tom , Scheres Sjors H. W. 
Published: August 2024

AbstractHow to deal with continuously flexing molecules is one of the biggest outstanding challenges in single-particle analysis of proteins from cryogenic-electron microscopy (cryo-EM) images. Here, we present DynaMight, a software tool that estimates a continuous space of conformations in a cryo-EM dataset by learning three-dimensional deformations of a Gaussian pseudo-atomic model of a consensus structure for every particle image. Inversion of the learned deformations is then used to obtain an improved reconstruction of the consensus structure. We illustrate the performance of DynaMight for several experimental cryo-EM datasets. We also show how error estimates on the deformations may be obtained by independently training two variational autoencoders on half sets of the cryo-EM data, and how regularization of the three-dimensional deformations through the use of atomic models may lead to important artifacts due to model bias. DynaMight is distributed as free, open-source software, as part of RELION-5.
Wearable high-density EMG sleeve for complex hand gesture classification and continuous joint angle estimation
Authors: Tacca Nicholas , Dunlap Collin , Donegan Sean P. , Hardin James O. , Meyers Eric , Darrow Michael J. , Colachis IV Samuel , Gillman Andrew , Friedenberg David A. 
Published: August 2024

AbstractHigh-density electromyography (HD-EMG) can provide a natural interface to enhance human–computer interaction (HCI). This study aims to demonstrate the capability of a novel HD-EMG forearm sleeve equipped with up to 150 electrodes to capture high-resolution muscle activity, decode complex hand gestures, and estimate continuous hand position via joint angle predictions. Ten able-bodied participants performed 37 hand movements and grasps while EMG was recorded using the HD-EMG sleeve. Simultaneously, an 18-sensor motion capture glove calculated 23 joint angles from the hand and fingers across all movements for training regression models. For classifying across the 37 gestures, our decoding algorithm was able to differentiate between sequential movements with$$97.3 \pm 0.3\%$$97.3±0.3%accuracy calculated on a 100 ms bin-by-bin basis. In a separate mixed dataset consisting of 19 movements randomly interspersed, decoding performance achieved an average bin-wise accuracy of$$92.8 \pm 0.8\%$$92.8±0.8%. When evaluating decoders for use in real-time scenarios, we found that decoders can reliably decode both movements and movement transitions, achieving an average accuracy of$$93.3 \pm 0.9\%$$93.3±0.9%on the sequential set and$$88.5 \pm 0.9\%$$88.5±0.9%on the mixed set. Furthermore, we estimated continuous joint angles from the EMG sleeve data, achieving a$$R^2$$R2of$$0.884 \pm 0.003$$0.884±0.003in the sequential set and$$0.750 \pm 0.008$$0.750±0.008in the mixed set. Median absolute error (MAE) was kept below 10° across all joints, with a grand average MAE of$$1.8 \pm 0.04^\circ$$1.8±0.04∘and$$3.4 \pm 0.07^\circ$$3.4±0.07∘for the sequential and mixed datasets, respectively. We also assessed two algorithm modifications to address specific challenges for EMG-driven HCI applications. To minimize decoder latency, we used a method that accounts for reaction time by dynamically shifting cue labels in time. To reduce training requirements, we show that pretraining models with historical data provided an increase in decoding performance compared with models that were not pretrained when reducing the in-session training data to only one attempt of each movement. The HD-EMG sleeve, combined with sophisticated machine learning algorithms, can be a powerful tool for hand gesture recognition and joint angle estimation. This technology holds significant promise for applications in HCI, such as prosthetics, assistive technology, rehabilitation, and human–robot collaboration.
A fractional-order model for optimizing combination therapy in heterogeneous lung cancer: integrating immunotherapy and targeted therapy to minimize side effects
Authors: Amilo David , Izuchukwu Chinedu , Sadri Khadijeh , Yao Hao-Ren , Hincal Evren , Shehu Yekini 
Published: August 2024

AbstractThis research presents a novel approach to address the complexities of heterogeneous lung cancer dynamics through the development of a Fractional-Order Model. Focusing on the optimization of combination therapy, the model integrates immunotherapy and targeted therapy with the specific aim of minimizing side effects. Notably, our approach incorporates a clever fusion of Proportional-Integral-Derivative (PID) feedback controls alongside the optimization process. Unlike previous studies, our model incorporates essential equations accounting for the interaction between regular and mutated cancer cells, delineates the dynamics between immune cells and mutated cancer cells, enhances immune cell cytotoxic activity, and elucidates the influence of genetic mutations on the spread of cancer cells. This refined model offers a comprehensive understanding of lung cancer progression, providing a valuable tool for the development of personalized and effective treatment strategies. the findings underscore the potential of the optimized treatment strategy in achieving key therapeutic goals, including primary tumor control, metastasis limitation, immune response enhancement, and controlled genetic mutations. The dynamic and adaptive nature of the treatment approach, coupled with economic considerations and memory effects, positions the research at the forefront of advancing precision and personalized cancer therapeutics.
Agricultural intensification in Lake Naivasha Catchment in Kenya and associated nutrients and pesticides pollution
Authors: Onyango Joel , Kitaka Nzula , van Bruggen J. J. A. , Irvine Kenneth , Simaika John 
Published: August 2024

AbstractInvestments in agricultural intensification in sub-Saharan Africa aim to fulfill food and economic demands. However, the increased use of fertilizers and pesticides poses ecological risks to water bodies in agricultural catchments. This study focused on assessing the impact of agricultural intensification on nutrient and pesticide pollution in the L. Naivasha catchment in Kenya. The research revealed significant changes in the catchment’s agricultural landscape between 1989 and 2019, driven by intensified agricultural expansion. As a result, nutrient and pesticide emissions have worsened the lake’s trophic status, shifting it towards hypereutrophic conditions. The study found a weak relationship between total nitrogen (TN) and sum dichlorodiphenyltrichloroethane (∑DDT), indicating that an increase in TN slightly predicted a reduction in ∑DDT. Analysis also showed potential phosphorus (P) limitation in the lake. Additionally, the observed ratio between dichlorodiphenyldichloroethane and dichlorodiphenyldichloroethylene (DDD:DDE) and (DDE + DDD):DDT ratios suggest recent use of banned DDT in the catchment. The study concludes that the transformation of L. Naivasha landscape shows unsustainable agricultural expansion with reduced forest cover, increased croplands, and increased pesticide contamination. This reflects a common issue in sub-Saharan Africa, that sustainable catchment management must address, specifically for combined pollutants, to support water quality and achieve the SDGs in agriculture.
Synchronicity of the Gulf Stream path downstream of Cape Hatteras and the region of maximum wind stress curl
Authors: Gifford Ian , Gangopadhyay Avijit , Andres Magdalena , Oliver Hilde , Gawarkiewicz Glen , Silver Adrienne 
Published: August 2024

AbstractThe Gulf Stream, a major ocean current in the North Atlantic ocean is a key component in the global redistribution of heat and is important for marine ecosystems. Based on 27 years (1993–2019) of wind reanalysis and satellite altimetry measurements, we present observational evidence that the path of this freely meandering jet after its separation from the continental slope at Cape Hatteras, aligns with the region of maximum cyclonic vorticity of the wind stress field known as the positive vorticity pool. This synchronicity between the wind stress curl maximum region and the Gulf Stream path is observed at multiple time-scales ranging from months to decades, spanning a distance of 1500 km between 70 and 55W. The wind stress curl in the positive vorticity pool is estimated to drive persistent upward vertical velocities ranging from 5 to 17 cm day−1 over its ~ 400,000 km2 area; this upwelling may supply a steady source of deep nutrients to the Slope Sea region, and can explain as much as a quarter of estimated primary productivity there.
XPS depth profiling of nano-layers by a novel trial-and-error evaluation procedure
Authors: Racz Adel Sarolta , Menyhard Miklos 
Published: August 2024

AbstractIn spite of its superior chemical sensitivity, XPS depth profiling is rarely used because of the alteration introduced by the sputter removal process and the resulting inhomogeneous in-depth concentration distribution. Moreover, the application of XPS becomes increasingly challenging in the case of the analysis of thin layers, if the thickness is in the range of 2–3 inelastic mean free paths (IMFP) of the photoelectrons. In this paper we will show that even in these unfavorable cases the XPS depth profiling is applicable. Herein the XPS depth profiling of a model system tungsten-carbide-rich nano-layer of high hardness and corrosion resistance is presented. We will show that the problems arising because of the relatively high IMFP can be corrected by introducing a layer model for the calculation of the observed XPS intensities, while the alteration, e.g. ion mixing, compound formation and similar artefact, introduced by the sputter removal process can be handled by TRIDYN simulation. The method presented here overcomes the limitation of XPS depth profiling.
Low-light image enhancement using generative adversarial networks
Authors: Wang Litian , Zhao Liquan , Zhong Tie , Wu Chunming 
Published: August 2024

AbstractIn low-light environments, the amount of light captured by the camera sensor is reduced, resulting in lower image brightness. This makes it difficult to recognize or completely lose details in the image, which affects subsequent processing of low-light images. Low-light image enhancement methods can increase image brightness while better-restoring color and detail information. A generative adversarial network is proposed for low-quality image enhancement to improve the quality of low-light images. This network consists of a generative network and an adversarial network. In the generative network, a multi-scale feature extraction module, which consists of dilated convolutions, regular convolutions, max pooling, and average pooling, is designed. This module can extract low-light image features from multiple scales, thereby obtaining richer feature information. Secondly, an illumination attention module is designed to reduce the interference of redundant features. This module assigns greater weight to important illumination features, enabling the network to extract illumination features more effectively. Finally, an encoder-decoder generative network is designed. It uses the multi-scale feature extraction module, illumination attention module, and other conventional modules to enhance low-light images and improve quality. Regarding the adversarial network, a dual-discriminator structure is designed. This network has a global adversarial network and a local adversarial network. They determine if the input image is actual or generated from global and local features, enhancing the performance of the generator network. Additionally, an improved loss function is proposed by introducing color loss and perceptual loss into the conventional loss function. It can better measure the color loss between the generated image and a normally illuminated image, thus reducing color distortion during the enhancement process. The proposed method, along with other methods, is tested using both synthesized and real low-light images. Experimental results show that, compared to other methods, the images enhanced by the proposed method are closer to normally illuminated images for synthetic low-light images. For real low-light images, the images enhanced by the proposed method retain more details, are more apparent, and exhibit higher performance metrics. Overall, compared to other methods, the proposed method demonstrates better image enhancement capabilities for both synthetic and real low-light images.
Shatianyu (Citrus grandis L. Osbeck) whole fruit alleviated loperamide-induced constipation via enhancing gut microbiota-mediated intestinal serotonin secretion and mucosal barrier homeostasis
Authors: Deng Mei , Ye Jiamin , Zhang Ruifen , Zhang Shuai , Dong Lihong , Su Dongxiao , Zhang Mingwei , Huang Fei 
Published: August 2024

The effects of Citrus grandis Shatianyu whole fruit powder enriched in flavonoids and dietary fiber on loperamide-induced constipation after a 4-week administration in the diet, together with possible microbiota-mediated mechanisms were explored.
Carbon market risk estimation using quantum conditional generative adversarial network and amplitude estimation
Authors: Zhou Xiyuan , Zhao Huan , Cao Yuji , Fei Xiang , Liang Gaoqi , Zhao Junhua 
Published: August 2024

AbstractAccurately and efficiently estimating the carbon market risk is paramount for ensuring financial stability, promoting environmental sustainability, and facilitating informed decision‐making. Although classical risk estimation methods are extensively utilized, the implicit pre‐assumptions regarding distribution are predominantly contained and challenging to balance accuracy and computational efficiency. A quantum computing‐based carbon market risk estimation framework is proposed to address this problem with the quantum conditional generative adversarial network‐quantum amplitude estimation (QCGAN‐QAE) algorithm. Specifically, quantum conditional generative adversarial network (QCGAN) is employed to simulate the future distribution of the generated return rate, whereas quantum amplitude estimation (QAE) is employed to measure the distribution. Moreover, the quantum circuit of the QCGAN improved by reordering the data interaction layer and data simulation layer is coupled with the introduction of the quantum fully connected layer. The binary search method is incorporated into the QAE to bolster the computational efficiency. The simulation results based on the European Union Emissions Trading System reveals that the proposed framework markedly enhances the efficiency and precision of value‐at‐risk and conditional value‐at‐risk compared to original methods.