bluuebunny/crossref_metadata_2025_split huggingface dataset:
A digital twin model incorporating generalized metabolic fluxes to identify and predict chronic kidney disease in type 2 diabetes mellitus
Authors: Surian Naveenah Udaya , Batagov Arsen , Wu Andrew , Lai Wen Bin , Sun Yan , Bee Yong Mong , Dalan Rinkoo 
Published: May 2024

AbstractWe have developed a digital twin-based CKD identification and prediction model that leverages generalized metabolic fluxes (GMF) for patients with Type 2 Diabetes Mellitus (T2DM). GMF digital twins utilized basic clinical and physiological biomarkers as inputs for identification and prediction of CKD. We employed four diverse multi-ethnic cohorts (n = 7072): a Singaporean cohort (EVAS, n = 289) and a North American cohort (NHANES, n = 1044) for baseline CKD identification, and two multi-center Singaporean cohorts (CDMD, n = 2119 and SDR, n = 3627) for 3-year CKD prediction and risk stratification. We subsequently conducted a comprehensive study utilizing a single dataset to evaluate the clinical utility of GMF for CKD prediction. The GMF-based identification model performed strongly, achieving an AUC between 0.80 and 0.82. In prediction, the GMF generated with complete parameters attained high performance with an AUC of 0.86, while with incomplete parameters, it achieved an AUC of 0.75. The GMF-based prediction model utilizing complete inputs is the standard implementation of our algorithm: HealthVector Diabetes®. We have established the GMF digital twin-based model as a robust clinical tool capable of predicting and stratifying the risk of future CKD within a 3-year time horizon. We report the correlation of GMF with basic input parameters, their ability to differentiate between future health states and medication status at baseline, and their capability to quantify CKD progression rates. This holistic methodology provides insights into patients’ health states and CKD progression rates based on GMF metabolic profile differences, enabling personalized care plans.
Imperceptible augmentation of living systems with organic bioelectronic fibres
Authors: Wang Wenyu , Pan Yifei , Shui Yuan , Hasan Tawfique , Lei Iek Man , Ka Stanley Gong Sheng , Savin Thierry , Velasco-Bosom Santiago , Cao Yang , McLaren Susannah B. P. , Cao Yuze , Xiong Fengzhu , Malliaras George G. , Huang Yan Yan Shery 
Published: May 2024

AbstractThe functional and sensory augmentation of living structures, such as human skin and plant epidermis, with electronics can be used to create platforms for health management and environmental monitoring. Ideally, such bioelectronic interfaces should not obstruct the inherent sensations and physiological changes of their hosts. The full life cycle of the interfaces should also be designed to minimize their environmental footprint. Here we report imperceptible augmentation of living systems through in situ tethering of organic bioelectronic fibres. Using an orbital spinning technique, substrate-free and open fibre networks—which are based on poly (3,4-ethylenedioxythiophene):polystyrene sulfonate—can be tethered to biological surfaces, including fingertips, chick embryos and plants. We use customizable fibre networks to create on-skin electrodes that can record electrocardiogram and electromyography signals, skin-gated organic electrochemical transistors and augmented touch and plant interfaces. We also show that the fibres can be used to couple prefabricated microelectronics and electronic textiles, and that the fibres can be repaired, upgraded and recycled.
CytoSIP: an annotated structural atlas for interactions involving cytokines or cytokine receptors
Authors: Wang Lu , Sun Fang , Li Qianying , Ma Haojie , Zhong Juanhong , Zhang Huihui , Cheng Siyi , Wu Hao , Zhao Yanmin , Wang Nasui , Xie Zhongqiu , Zhao Mingyi , Zhu Ping , Zheng Heping 
Published: May 2024

AbstractTherapeutic agents targeting cytokine-cytokine receptor (CK-CKR) interactions lead to the disruption in cellular signaling and are effective in treating many diseases including tumors. However, a lack of universal and quick access to annotated structural surface regions on CK/CKR has limited the progress of a structure-driven approach in developing targeted macromolecular drugs and precision medicine therapeutics. Herein we develop CytoSIP (Single nucleotide polymorphisms (SNPs), Interface, and Phenotype), a rich internet application based on a database of atomic interactions around hotspots in experimentally determined CK/CKR structural complexes. CytoSIP contains: (1) SNPs on CK/CKR; (2) interactions involving CK/CKR domains, including CK/CKR interfaces, oligomeric interfaces, epitopes, or other drug targeting surfaces; and (3) diseases and phenotypes associated with CK/CKR or SNPs. The database framework introduces a unique tri-level SIP data model to bridge genetic variants (atomic level) to disease phenotypes (organism level) using protein structure (complexes) as an underlying framework (molecule level). Customized screening tools are implemented to retrieve relevant CK/CKR subset, which reduces the time and resources needed to interrogate large datasets involving CK/CKR surface hotspots and associated pathologies. CytoSIP portal is publicly accessible at https://CytoSIP.biocloud.top, facilitating the panoramic investigation of the context-dependent crosstalk between CK/CKR and the development of targeted therapeutic agents.
In vivo cyclic overexpression of Yamanaka factors restricted to neurons reverses age-associated phenotypes and enhances memory performance
Authors: Antón-Fernández Alejandro , Roldán-Lázaro Marta , Vallés-Saiz Laura , Ávila Jesús , Hernández Félix 
Published: May 2024

AbstractIn recent years, there has been success in partially reprogramming peripheral organ cells using cyclic Yamanaka transcription factor (YF) expression, resulting in the reversal of age-related pathologies. In the case of the brain, the effects of partial reprogramming are scarcely known, and only some of its effects have been observed through the widespread expression of YF. This study is the first to exclusively partially reprogram a specific subpopulation of neurons in the cerebral cortex of aged mice. The in vivo model demonstrate that YF expression in postmitotic neurons does not dedifferentiate them, and it avoids deleterious effects observed with YF expression in other cell types. Additionally, our study demonstrates that only cyclic, not continuous, expression of YF result in a noteworthy enhancement of cognitive function in adult mice. This enhancement is closely tied to increased neuronal activation in regions related to memory processes, reversed aging-related epigenetic markers and to increased plasticity, induced by the reorganization of the extracellular matrix. These findings support the therapeutic potential of targeted partial reprogramming of neurons in addressing age-associated phenotypes and neurodegenerative diseases correlated with aging.
In vitro generation of genetic diversity for directed evolution by error-prone artificial DNA synthesis
Authors: Wang Baowei , Liu Yang , Bai Xuelian , Tian Huijuan , Wang Lina , Feng Miao , Xia Hairong 
Published: May 2024

AbstractGenerating genetic diversity lies at the heart of directed evolution which has been widely used to engineer genetic parts and gene circuits in synthetic biology. With the ever-expanding application of directed evolution, different approaches of generating genetic diversity are required to enrich the traditional toolbox. Here we show in vitro generation of genetic diversity for directed evolution by error-prone artificial DNA synthesis (epADS). This approach comprises a three-step process which incorporates base errors randomly generated during chemical synthesis of oligonucleotides under specific conditions into the target DNA. Through this method, 200 ~ 4000 folds of diversification in fluorescent strength have been achieved in genes encoding fluorescent proteins. EpADS has also been successfully used to diversify regulatory genetic parts, synthetic gene circuits and even increase microbial tolerance to carbenicillin in a short time period. EpADS would be an alternative tool for directed evolution which may have useful applications in synthetic biology.
Weak neuronal glycolysis sustains cognition and organismal fitness
Authors: Jimenez-Blasco Daniel , Agulla Jesús , Lapresa Rebeca , Garcia-Macia Marina , Bobo-Jimenez Veronica , Garcia-Rodriguez Dario , Manjarres-Raza Israel , Fernandez Emilio , Jeanson Yannick , Khoury Spiro , Portais Jean-Charles , Padro Daniel , Ramos-Cabrer Pedro , Carmeliet Peter , Almeida Angeles , Bolaños Juan P. 
Published: May 2024

AbstractThe energy cost of neuronal activity is mainly sustained by glucose1,2. However, in an apparent paradox, neurons modestly metabolize glucose through glycolysis3–6, a circumstance that can be accounted for by the constant degradation of 6-phosphofructo-2-kinase–fructose-2,6-bisphosphatase-3 (PFKFB3)3,7,8, a key glycolysis-promoting enzyme. To evaluate the in vivo physiological importance of this hypoglycolytic metabolism, here we genetically engineered mice with their neurons transformed into active glycolytic cells through Pfkfb3 expression. In vivo molecular, biochemical and metabolic flux analyses of these neurons revealed an accumulation of anomalous mitochondria, complex I disassembly, bioenergetic deficiency and mitochondrial redox stress. Notably, glycolysis-mediated nicotinamide adenine dinucleotide (NAD+) reduction impaired sirtuin-dependent autophagy. Furthermore, these mice displayed cognitive decline and a metabolic syndrome that was mimicked by confining Pfkfb3 expression to hypothalamic neurons. Neuron-specific genetic ablation of mitochondrial redox stress or brain NAD+ restoration corrected these behavioural alterations. Thus, the weak glycolytic nature of neurons is required to sustain higher-order organismal functions.
Super phantoms: advanced models for testing medical imaging technologies
Authors: Manohar Srirang , Sechopoulos Ioannis , Anastasio Mark A. , Maier-Hein Lena , Gupta Rajiv 
Published: May 2024

AbstractPhantoms are test objects used for initial testing and optimization of medical imaging techniques, but these rarely capture the complex properties of the tissue. Here we introduce super phantoms, that surpass standard phantoms being able to replicate complex anatomic and functional imaging properties of tissues and organs. These super phantoms can be computer models, inanimate physical objects, or ex-vivo organs. Testing on these super phantoms, will enable iterative improvements well before in-vivo studies, fostering innovation. We illustrate super phantom examples, address development challenges, and envision centralized facilities supporting multiple institutions in applying these models for medical advancements.
Deliberately ignoring inequality to avoid rejecting unfair offers
Authors: Offer Konstantin , Mischkowski Dorothee , Rahwan Zoe , Engel Christoph 
Published: May 2024

AbstractWhy do people punish experienced unfairness if it induces costs for both the punisher and punished person(s) without any direct material benefits for the punisher? Economic theories of fairness propose that punishers experience disutility from disadvantageous inequality and punish in order to establish equality in outcomes. We tested these theories in a modified Ultimatum Game (N = 1370) by examining whether people avoid the urge to reject unfair offers, and thereby punish the proposer, by deliberately blinding themselves to unfairness. We found that 53% of participants deliberately ignored whether they had received an unfair offer. Among these participants, only 6% of offers were rejected. As expected, participants who actively sought information rejected significantly more unfair offers (39%). Averaging these rejection rates to 21%, no significant difference to the rejection rate by participants who were directly informed about unfairness was found, contrary to our hypothesis. We interpret these findings as evidence for sorting behavior: People who punish experienced unfairness seek information about it, while those who do not punish deliberately ignore it.
Increasing psychopharmacology clinical trial success rates with digital measures and biomarkers: Future methods
Authors: Reiter Jacob E. , Nickels Stefanie , Nelson Benjamin W. , Rainaldi Erin , Peng Lily , Doraiswamy P. Murali , Kapur Ritu , Abernethy Amy , Trister Andrew 
Published: May 2024

AbstractPsychiatric trials have some of the lowest success rates across therapeutic areas, resulting in decreased investment in psychopharmacological drug development even as the need for more effective treatments grows. Digital measures and digital biomarkers (DBMs) provide one potential avenue for ameliorating three of the largest problems impeding clinical trial success in psychiatry: diagnostic heterogeneity, endpoint subjectivity, and high placebo response rates. First, DBMs may address heterogeneity and comorbidity in psychiatric nosology by identifying predictive DBMs of treatment response via the targeting of drugs to psychiatric subtypes. Second, DBMs can provide objective measures of physiology and behavior that when grounded in meaningful aspects of health (MAH) could support use for regulatory decision-making. By objectively and continuously measuring aspects of a patient’s disease that the patient wants to improve or prevent from getting worse, DBMs might provide clinical trial endpoints that are more sensitive to treatment effects as compared to traditional clinician-reported outcomes. Lastly, DBMs could help address challenges surrounding high placebo response rates. Development of predictive DBMs of placebo response may allow for improved enrichment study designs to reduce placebo response. Objective digital measures may also be more robust against the placebo effect and offer an improved study endpoint alternative. Successful deployment of DBMs to address the historical challenges facing psychiatric drug trials will require close collaboration between industry, academic, and regulatory partners.
Neuraminidase inhibition promotes the collective migration of neurons and recovery of brain function
Authors: Matsumoto Mami , Matsushita Katsuyoshi , Hane Masaya , Wen Chentao , Kurematsu Chihiro , Ota Haruko , Bang Nguyen Huy , Quynh Thai Truc , Herranz-Pérez Vicente , Sawada Masato , Fujimoto Koichi , García-Verdugo José Manuel , Kimura Koutarou D , Seki Tatsunori , Sato Chihiro , Ohno Nobuhiko , Sawamoto Kazunobu 
Published: May 2024

AbstractIn the injured brain, new neurons produced from endogenous neural stem cells form chains and migrate to injured areas and contribute to the regeneration of lost neurons. However, this endogenous regenerative capacity of the brain has not yet been leveraged for the treatment of brain injury. Here, we show that in healthy brain chains of migrating new neurons maintain unexpectedly large non-adherent areas between neighboring cells, allowing for efficient migration. In instances of brain injury, neuraminidase reduces polysialic acid levels, which negatively regulates adhesion, leading to increased cell–cell adhesion and reduced migration efficiency. The administration of zanamivir, a neuraminidase inhibitor used for influenza treatment, promotes neuronal migration toward damaged regions, fosters neuronal regeneration, and facilitates functional recovery. Together, these findings shed light on a new mechanism governing efficient neuronal migration in the adult brain under physiological conditions, pinpoint the disruption of this mechanism during brain injury, and propose a promising therapeutic avenue for brain injury through drug repositioning.
Culturally Responsive Literacy Interventions in Speech-Language Pathology: A Scoping Review
Authors: Guiberson Mark 
Published: May 2024

Purpose and Method:
In this study, the author employed a positivist perspective and framework, utilizing a scoping methodology to systematically explore the extant research literature in speech-language pathology pertaining to literacy interventions for children from culturally and linguistically diverse (CLD) backgrounds.

Results:
The scoping review encompassed 22 studies selected from a pool of 908 sources, uncovering 34 literacy strategies presented in an evidence map. These strategies, intended for both school-age and preschool children, were implemented across diverse populations, including Indigenous communities, dual language learners, and children with disabilities. The strategies identified offer valuable insights for intervention planning and piloting.

Conclusions:
The review underscores the significance of culturally responsive literacy interventions in fostering inclusive and supportive learning environments for CLD learners. Additionally, two illustrative case studies are presented to exemplify the application of the identified strategies and discussed in the context of implementing Universal Design for Learning principles for all students.

Supplemental Material:

https://doi.org/10.23641/asha.25864624
A chaotic time series combined prediction model for improving trend lagging
Authors: Liu Fang , Zheng Yuanfang , Chen Lizhi , Feng Yongxin 
Published: May 2024

AbstractChaotic time series prediction is a prediction method based on chaos theory, and has important theoretical and application value. At present, most prediction methods only pursue digital fitting and do not consider the directional trend. In addition, using the single model will not achieve better prediction results. Therefore, a chaotic time series combined prediction model for improving trend lagging (ITL) is proposed. An improved dual‐stage attention‐based long short‐term memory model with the improved training objective fuction is designed to solve the trend lagging problem. Then, an auto regressive moving average model with the sliding window is established to mine other characteristics of the time series except nonlinear characteristic. Finally, the idea of optimization algorithm is introduced to construct a time series combined prediction model with high accuracy based on the above two models, so as to perform the chaotic time series prediction from multiple perspectives. Multiple datasets are selected as experimental datasets, and the proposed method is compared with common prediction methods. The results show that the proposed method can achieve single‐step prediction with high accuracy and effectively improve the lagging of chaotic time series prediction. This research can provide theoretical support for the complex chaotic time series prediction.
Multi‐view synergistic enhanced fault recording data for transmission line fault classification
Authors: Jia Minghui , Huang Xiaohu , Han Fengjun , Yan Dequan , Wang Wei , Zhu Guochao , Zhang Lin , Pan Chao , Ma Haifeng , Wei Jidong 
Published: May 2024

AbstractFault recorded data has been proven to be effective for fault diagnosis of overhead transmission lines. Utilizing deep learning to mine potential fault patterns in fault recording data is an inevitable trend. However, it is usually difficult to obtain massive labeled fault recording data, which results in deep learning‐based fault diagnosis models not being adequately trained. Although data augmentation methods provide ideas for expanding the training data, existing data augmentation algorithms (e.g. random perturbation‐based augmentation) may lead to distortion of multi‐view data, that is, time domain data and frequency domain data of the fault recorded data, which results in the inconsistency of physical properties and statistical distributions of the generated data and the actual recording data, and misguides the training of the models. Hence, this study proposes a transmission line fault classification method via the multi‐view synergistic enhancement of fault recording data. The methodology proposes to start with a synergistic enhancement of multi‐view data such as time and frequency domains of fault recording data, and utilizes contrastive learning to further improve the performance of the fault classification model while ensuring that the generated data is not distorted. Experimental results on three real‐world datasets validate the effectiveness of the proposed method.
The online control method of independent DC microgrid based on virtual hydrogen consumption method
Authors: Wu Yongjun , Zhang Zhiyuan , Li Xiqin , Wang Bin , Zhu Xiaowen , Jing Yuchao , Shi Mengxiang , Liu Chaofan 
Published: May 2024

AbstractWith the development of distributed generation and power electronics technology, DC microgrids have made certain progress with their advantages of simple structure, economic efficiency, and easy control. Considering factors such as the geographical location of some residential areas, microgrids with fuel cells that can operate independently are also receiving increasing attention. In this paper, an online control method named virtual hydrogen consumption is proposed based on an independent DC microgrid which solves the problem of electro‐hydrogen conversion successfully. By solving the overall consumption function, the analytical solution is further obtained to realize the online control. Based on the results of RT‐LAB platform, the proposed method can maintain the output of fuel cell system in its high efficiency range with a lower volatility.
Efficient gridless 2D DOA estimation based on generalized matrix‐form atomic norm minimization
Authors: Gao Silin , Wang Muhan , Zhang Zhe , Zhang Bingchen , Wu Yirong 
Published: May 2024

AbstractTwo‐dimensional (2D) direction‐of‐arrival (DOA) estimation is crucial in array signal processing. Compressed sensing (CS) provides a superior alternative to spatial spectrum estimation algorithms by enabling 2D DOA estimation of correlated sources from single snapshot data. However, the grid mismatch effect inherent in grid‐based CS algorithms impacts estimation accuracy. Despite recent advancements, the state‐of‐the‐art gridless CS algorithm, decoupled atomic norm minimization, is limited to specific 2D array geometries, such as uniform rectangular arrays. This letter presents an efficient gridless 2D DOA estimation algorithm for generalized rectangular arrays, including both uniform and sparse arrays. The proposed algorithm achieves high accuracy through a novel approach called generalized matrix‐form atomic norm minimization and provides a fast solution using the alternating direction method of multipliers. Validation through computer simulations and practical experiments underscores its efficacy.
A chirplet‐based masking algorithm for smeared spectrum jamming suppression and signal separation
Authors: Wang Yifan , Li Yibing , Yu Gang , Geng Xiaoyu , Zhou Zitao 
Published: May 2024

AbstractLinear frequency modulation (LFM) signal is a common radar signal in modern electronic warfare, and smeared spectrum (SMSP) can generate multiple false targets, causing jamming to radar detection. The authors propose a chirplet‐based masking algorithm that can solve the problem of SMSP jamming suppression and address a more complex problem: the separation of jamming signal and multiple LFM signals from intercepted mixed signal. First, the authors obtain matched chirp rates of the source signals through the changing tendency of the Rényi entropy. Then, the ridge of each source signal is extracted from the high‐resolution chirplet transform result using an image processing‐based algorithm. Finally, the jamming and LFM signals are accurately reconstructed through the time‐frequency mask to achieve separation. Even in the extreme case where multiple source signals with close chirp rates are overlapped, the proposed slope‐matching ridge extraction method and iterative update reconstruction method can still achieve commendable signal separation effects. Extensive experimental results demonstrate that the proposed algorithm performs well under extreme conditions of low signal‐to‐noise ratio, high jamming‐to‐signal ratio, and high sea state.