bluuebunny/crossref_metadata_2025_split huggingface dataset:
Integrated network pharmacology and experimental validation to explore the mechanisms underlying naringenin treatment of chronic wounds
Authors: Sun Rui , Liu Chunyan , Liu Jian , Yin Siyuan , Song Ru , Ma Jiaxu , Cao Guoqi , Lu Yongpan , Zhang Guang , Wu Zhenjie , Chen Aoyu , Wang Yibing 
Published: January 2023

AbstractNaringenin is a citrus flavonoid with various biological functions and a potential therapeutic agent for skin diseases, such as UV radiation and atopic dermatitis. The present study investigates the therapeutic effect and pharmacological mechanism of naringenin on chronic wounds. Using network pharmacology, we identified 163 potential targets and 12 key targets of naringenin. Oxidative stress was confirmed to be the main biological process modulated by naringenin. The transcription factor p65 (RELA), alpha serine/threonine-protein kinase (AKT1), mitogen-activated protein kinase 1 (MAPK1) and mitogen-activated protein kinase 3 (MAPK3) were identified as common targets of multiple pathways involved in treating chronic wounds. Molecular docking verified that these four targets stably bound naringenin. Naringenin promoted wound healing in mice in vivo by inhibiting wound inflammation. Furthermore, in vitro experiments showed that a low naringenin concentration did not significantly affect normal skin cell viability and cell apoptosis; a high naringenin concentration was cytotoxic and reduced cell survival by promoting apoptosis. Meanwhile, comprehensive network pharmacology, molecular docking and in vivo and in vitro experiments revealed that naringenin could treat chronic wounds by alleviating oxidative stress and reducing the inflammatory response. The underlying mechanism of naringenin in chronic wound therapy involved modulating the RELA, AKT1 and MAPK1/3 signalling pathways to inhibit ROS production and inflammatory cytokine expression.
Social feedback enhances learning in Williams syndrome
Authors: Kleberg Johan Lundin , Willfors Charlotte , Björlin Avdic Hanna , Riby Deborah , Galazka Martyna A. , Guath Mona , Nordgren Ann , Strannegård Claes 
Published: January 2023

AbstractWilliams syndrome (WS) is a rare genetic condition characterized by high social interest and approach motivation as well as intellectual disability and anxiety. Despite the fact that social stimuli are believed to have an increased intrinsic reward value in WS, it is not known whether this translates to learning and decision making. Genes homozygously deleted in WS are linked to sociability in the general population, making it a potential model condition for understanding the social brain. Probabilistic reinforcement learning was studied with either social or non-social rewards for correct choices. Social feedback improved learning in individuals with Williams syndrome but not in typically developing controls or individuals with other intellectual disabilities. Computational modeling indicated that these effects on social feedback were mediated by a shift towards higher weight given to rewards relative to punishments and increased choice consistency. We conclude that reward learning in WS is characterized by high volatility and a tendency to learn how to avoid punishment rather than how to gain rewards. Social feedback can partly normalize this pattern and promote adaptive reward learning.
Effects of the exercise-inducible myokine irisin on proliferation and malignant properties of ovarian cancer cells through the HIF-1 α signaling pathway
Authors: Alizadeh Zarei Marziyeh , Seyed Hosseini Elahe , Haddad Kashani Hamed , Ahmad Ejaz , Nikzad Hossein 
Published: January 2023

AbstractExercise has been shown to be associated with reduced risk and improving outcomes of several types of cancers. Irisin -a novel exercise-related myokine- has been proposed to exert beneficial effects in metabolic disorders including cancer. No previous studies have investigated whether irisin may regulate malignant characteristics of ovarian cancer cell lines. In the present study, we aimed to explore the effect of irisin on viability and proliferation of ovarian cancer cells which was examined by MTT assay. Then, we evaluated the migratory and invasive abilities of the cells via transwell assays. Moreover, the percentage of apoptosis induction was determined by flow cytometry. Furthermore, the mRNA expression level of genes related to the aerobic respiration (HIF-1α, c-Myc, LDHA, PDK1 and VEGF) was detected by real-time PCR. Our data revealed that irisin treatment significantly attenuated the proliferation, migration and invasion of ovarian cancer cells. Additionally, irisin induced apoptosis in ovarian cancer cells. We also observed that irisin regulated the expression of genes involved in aerobic respiration of ovarian cancer cells. Our results indicated that irisin may play a crucial role in inhibition of cell growth and malignant characteristics of ovarian cancer. These findings may open up avenues for future studies to identify the further therapeutic use of irisin in ovarian cancer management.
End-to-end differentiable blind tip reconstruction for noisy atomic force microscopy images
Authors: Matsunaga Yasuhiro , Fuchigami Sotaro , Ogane Tomonori , Takada Shoji 
Published: January 2023

AbstractObserving the structural dynamics of biomolecules is vital to deepening our understanding of biomolecular functions. High-speed (HS) atomic force microscopy (AFM) is a powerful method to measure biomolecular behavior at near physiological conditions. In the AFM, measured image profiles on a molecular surface are distorted by the tip shape through the interactions between the tip and molecule. Once the tip shape is known, AFM images can be approximately deconvolved to reconstruct the surface geometry of the sample molecule. Thus, knowing the correct tip shape is an important issue in the AFM image analysis. The blind tip reconstruction (BTR) method developed by Villarrubia (J Res Natl Inst Stand Technol 102:425, 1997) is an algorithm that estimates tip shape only from AFM images using mathematical morphology operators. While the BTR works perfectly for noise-free AFM images, the algorithm is susceptible to noise. To overcome this issue, we here propose an alternative BTR method, called end-to-end differentiable BTR, based on a modern machine learning approach. In the method, we introduce a loss function including a regularization term to prevent overfitting to noise, and the tip shape is optimized with automatic differentiation and backpropagations developed in deep learning frameworks. Using noisy pseudo-AFM images of myosin V motor domain as test cases, we show that our end-to-end differentiable BTR is robust against noise in AFM images. The method can also detect a double-tip shape and deconvolve doubled molecular images. Finally, application to real HS-AFM data of myosin V walking on an actin filament shows that the method can reconstruct the accurate surface geometry of actomyosin consistent with the structural model. Our method serves as a general post-processing for reconstructing hidden molecular surfaces from any AFM images. Codes are available at https://github.com/matsunagalab/differentiable_BTR.
GSDMD deficiency ameliorates hyperoxia-induced BPD and ROP in neonatal mice
Authors: Sonny Sarah , Yuan Huijun , Chen Shaoyi , Duncan Matthew R. , Chen Pingping , Benny Merline , Young Karen , Park Kevin K. , Schmidt Augusto F. , Wu Shu 
Published: January 2023

AbstractBronchopulmonary dysplasia (BPD) and retinopathy of prematurity (ROP) are among the most common morbidities affecting extremely premature infants who receive oxygen therapy. Many clinical studies indicate that BPD is associated with advanced ROP. However, the mechanistic link between hyperoxia, BPD, and ROP remains to be explored. Gasdermin D (GSDMD) is a key executor of inflammasome-induced pyroptosis and inflammation. Inhibition of GSDMD has been shown to attenuate hyperoxia-induced BPD and brain injury in neonatal mice. The objective of this study was to further define the mechanistic roles of GSDMD in the pathogenesis of hyperoxia-induced BPD and ROP in mouse models. Here we show that global GSDMD knockout (GSDMD-KO) protects against hyperoxia-induced BPD by reducing macrophage infiltration, improving alveolarization and vascular development, and decreasing cell death. In addition, GSDMD deficiency prevented hyperoxia-induced ROP by reducing vasoobliteration and neovascularization, improving thinning of multiple retinal tissue layers, and decreasing microglial activation. RNA sequencing analyses of lungs and retinas showed that similar genes, including those from inflammatory, cell death, tissue remodeling, and tissue and vascular developmental signaling pathways, were induced by hyperoxia and impacted by GSDMD-KO in both models. These data highlight the importance of GSDMD in the pathogenesis of BPD and ROP and suggest that targeting GSDMD may be beneficial in preventing and treating BPD and ROP in premature infants.
Generating multi-temporal landslide inventories through a general deep transfer learning strategy using HR EO data
Authors: Bhuyan Kushanav , Tanyaş Hakan , Nava Lorenzo , Puliero Silvia , Meena Sansar Raj , Floris Mario , van Westen Cees , Catani Filippo 
Published: January 2023

AbstractMapping of landslides over space has seen an increasing attention and good results in the last decade. While current methods are chiefly applied to generate event-inventories, whereas multi-temporal (MT) inventories are rare, even using manual landslide mapping. Here, we present an innovative deep learning strategy which employs transfer learning that allows for the Attention Deep Supervision Multi-Scale U-Net model to be adapted for landslide detection tasks in new areas. The method also provides the flexibility of re-training a pretrained model to detect both rainfall- and earthquake-triggered landslides on new target areas. For the mapping, we used archived Planet Lab remote sensing images spanning a period between 2009 till 2021 with spatial resolution of 3–5 m to systematically generate MT landslide inventories. When we examined all cases, our approach provided an average F1 score of 0.8 indicating that we successfully identified the spatiotemporal occurrences of landslides. To examine the size distribution of mapped landslides we compared the frequency-area distributions of predicted co-seismic landslides with manually mapped products from the literature. Results showed a good match between calculated power-law exponents where the difference ranges between 0.04 and 0.21. Overall, this study showed that the proposed algorithm could be applied to large areas to generate polygon-based MT landslide inventories.
Association between different sensory modalities based on concurrent time series data obtained by a collaborative reservoir computing model
Authors: Kanemura Itsuki , Kitano Katsunori 
Published: January 2023

AbstractHumans perceive the external world by integrating information from different modalities, obtained through the sensory organs. However, the aforementioned mechanism is still unclear and has been a subject of widespread interest in the fields of psychology and brain science. A model using two reservoir computing systems, i.e., a type of recurrent neural network trained to mimic each other's output, can detect stimulus patterns that repeatedly appear in a time series signal. We applied this model for identifying specific patterns that co-occur between information from different modalities. The model was self-organized by specific fluctuation patterns that co-occurred between different modalities, and could detect each fluctuation pattern. Additionally, similarly to the case where perception is influenced by synchronous/asynchronous presentation of multimodal stimuli, the model failed to work correctly for signals that did not co-occur with corresponding fluctuation patterns. Recent experimental studies have suggested that direct interaction between different sensory systems is important for multisensory integration, in addition to top-down control from higher brain regions such as the association cortex. Because several patterns of interaction between sensory modules can be incorporated into the employed model, we were able to compare the performance between them; the original version of the employed model incorporated such an interaction as the teaching signals for learning. The performance of the original and alternative models was evaluated, and the original model was found to perform the best. Thus, we demonstrated that feedback of the outputs of appropriately learned sensory modules performed the best when compared to the other examined patterns of interaction. The proposed model incorporated information encoded by the dynamic state of the neural population and the interactions between different sensory modules, both of which were based on recent experimental observations; this allowed us to study the influence of the temporal relationship and frequency of occurrence of multisensory signals on sensory integration, as well as the nature of interaction between different sensory signals.
Precision medicine: affording the successes of science
Authors: Lu Christine Y. , Terry Vera , Thomas David M. 
Published: January 2023

AbstractScience has made remarkable advances in understanding the molecular basis of disease, generating new and effective rationally-designed treatments at an accelerating rate. Ironically, the successes of science is creating a crisis in the affordability of equitable health care. The COVID-19 pandemic underscores both the value of science in health care, and the apparently inevitable tension between health and the economy. Drug development in ever-smaller target populations is a critical component of the rising costs of care. For structural and historical reasons, drug development is inefficient and poorly integrated across the public and private sectors. We postulate an alternative, integrated model in which governments and industry share the risks and benefits of drug development. The Australian government recently announced support for a AU$185 million innovative multi-stakeholder public-private partnership model for sustainable precision oncology, accelerating biomarker-dependent drug development through integrating clinical trials into the standard of care.
Human theca arises from ovarian stroma and is comprised of three discrete subtypes
Authors: Guahmich Nicole Lustgarten , Man Limor , Wang Jerry , Arazi Laury , Kallinos Eleni , Topper-Kroog Ariana , Grullon Gabriel , Zhang Kimberly , Stewart Joshua , Schatz-Siemers Nina , Jones Sam H. , Bodine Richard , Zaninovic Nikica , Schattman Glenn , Rosenwaks Zev , James Daylon 
Published: January 2023

AbstractTheca cells serve multiple essential functions during the growth and maturation of ovarian follicles, providing structural, metabolic, and steroidogenic support. While the function of theca during folliculogenesis is well established, their cellular origins and the differentiation hierarchy that generates distinct theca sub-types, remain unknown. Here, we performed single cell multi-omics analysis of primary cell populations purified from human antral stage follicles (1–3 mm) to define the differentiation trajectory of theca/stroma cells. We then corroborated the temporal emergence and growth kinetics of defined theca/stroma subpopulations using human ovarian tissue samples and xenografts of cryopreserved/thawed ovarian cortex, respectively. We identified three lineage specific derivatives termed structural, androgenic, and perifollicular theca cells, as well as their putative lineage-negative progenitor. These findings provide a framework for understanding the differentiation process that occurs in each primordial follicle and identifies specific cellular/molecular phenotypes that may be relevant to either diagnosis or treatment of ovarian pathologies.
Comparative study of adaptive variational quantum eigensolvers for multi-orbital impurity models
Authors: Mukherjee Anirban , Berthusen Noah F. , Getelina João C. , Orth Peter P. , Yao Yong-Xin 
Published: January 2023

AbstractHybrid quantum-classical embedding methods for correlated materials simulations provide a path towards potential quantum advantage. However, the required quantum resources arising from the multi-band nature ofdandfelectron materials remain largely unexplored. Here we compare the performance of different variational quantum eigensolvers in ground state preparation for interacting multi-orbital embedding impurity models, which is the computationally most demanding step in quantum embedding theories. Focusing on adaptive algorithms and models with 8 spin-orbitals, we show that state preparation with fidelities better than 99.9% can be achieved using about 214shots per measurement circuit. When including gate noise, we observe that parameter optimizations can still be performed if the two-qubit gate error lies below 10−3, which is slightly smaller than current hardware levels. Finally, we measure the ground state energy on IBM and Quantinuum hardware using a converged adaptive ansatz and obtain a relative error of 0.7%.
The Impact of Academic, Sociodemographic, and Program Growth Factors on Admission Offers to U.S. Graduate Education Programs in Communication Sciences and Disorders: National Trends in 2016–2020 Cycles
Authors: Watts Christopher R. , DiLollo Anthony , Zhang Yan 
Published: January 2023

Purpose:
The purpose of this study was to investigate the impact of academic factors and sociodemographic factors on offers of admission to graduate education programs in communication sciences and disorders (speech-language pathology and audiology) in the United States.

Method:
A retrospective analysis of extant data from undergraduate students applying to graduate education programs through the Communication Sciences and Disorders Centralized Application Service (CSDCAS) was conducted. Descriptive, parametric, nonparametric, and multivariate hierarchical logistic modeling analyses were applied to data from 38,625 unique applicants across four consecutive application cycles from 2016 to 2020 to assess relationships between admission offers, and academic and sociodemographic factors. The academic factors included Graduate Record Examination (GRE) and grade point average (GPA; cumulative undergraduate GPA and cumulative communication sciences and disorders [CSD] GPA), and sociodemographic factors included race/ethnicity, age, disadvantaged socioeconomic status, first-generation status, and multilinguistic status.

Results:

The rate of receiving an offer of admission continuously increased from 59.4% in the 2016–2017 cycle to 75.4% in the 2019–2020 cycle (
p
< .001). The significant predictors for admission offers across all four application cycles were GPA, GRE, and applicant age. While the odds ratios of GRE and age were relatively stable, the odds ratios of GPA had a decreasing trend. Bivariate analyses showed that students who were non-White, older, socioeconomically disadvantaged, first-generation, and nonmultilingual were significantly less likely to receive offers of admission than their counterparts, but the relationships between those sociodemographic factors, except for age, and admission offers diminished when all factors were considered in the logistic regression analyses.

Conclusions:
Academic and sociodemographic factors significantly affected the likelihood of obtaining at least one offer of admission to a graduate program in CSD at different levels. While the effect sizes were variable, these findings provide evidence-based guidance for admission committees seeking to improve the inclusiveness of admission processes and the realization of greater diversity across multidimensional domains (e.g., race/ethnicity, socioeconomic status, age).
Identifying Linguistic Markers of French-Speaking Teenagers With Developmental Language Disorder: Which Tasks Matter?
Authors: Courteau Émilie , Loignon Guillaume , Steinhauer Karsten , Royle Phaedra 
Published: January 2023

Purpose:
This research aimed to identify reliable tasks discriminating French-speaking adolescents with developmental language disorder (DLD) from their peers with typical language (TL) and to assess which linguistic domains represent areas of particular weakness in DLD. Unlike English, morphosyntax has not been identified as a special area of weakness when compared with lexicosemantics in French preschoolers with DLD. Since there is evidence that subject–verb number agreement is consolidated in later childhood, one might expect morphosyntax to be a particular weakness and marker of French DLD only in (pre)adolescence.

Method:

We administered 20 subtasks that assessed linguistic and phonological working memory skills of two groups: 17 adolescents clinically identified as having DLD (
M
= 14.1 years) and 20 (pre)teens with TL (
M
= 12.2 years). Using robust statistics that are less affected by outliers, we selected the most discriminating subtasks between our groups, calculated their optimal cutoff score, and derived diagnostic accuracy statistics. We combined these subtasks in a multivariable model to identify which subtasks contributed the most to the identification of DLD.

Results:
Seven subtasks were selected as discriminating between our groups, and three showed outstanding diagnostic accuracy: Recalling Sentences, a multiword task assessing lexicosemantic skills, and a subject–verb number agreement production task. When combined, we found that the latter contributed the most to our multivariable model.

Conclusion:
This study provides evidence that the most relevant markers to identify DLD in French teenagers are tasks assessing lexicosemantics and morphosyntactic domains, and that morphosyntax should be considered an important area of weakness in French-speaking teenagers with DLD.

Supplemental Material:

https://doi.org/10.23641/asha.21753932
Eliminating Racial Bias and Challenges for Black Athletes With Sports-Related Concussion: The Role of Speech-Language Pathology Clinicians and Researchers
Authors: Davis Alaina S. 
Published: January 2023

Purpose:
The aim of this tutorial is to highlight racial bias and challenges for Black adult athletes' cognitive-communication disorders associated with sports-related concussion (SRC). The author discusses racial bias in SRC literature and research and cultural differences in the cognitive and communication styles of Black athletes that lead to bias. The tutorial highlights challenges placed on the Black athletes when culturally responsive practices are not used in research and clinical practice. Finally, speech-language pathology clinicians and researchers are provided with practical solutions to decrease racial barriers in SRC research and clinical practice.

Conclusion:
Challenges for Black adult athletes with SRC can be overcome when speech-language pathology clinicians and researchers increase their awareness of what causes racial health disparities, eliminate beliefs in stereotypes centered around the cognitive and communication styles of Black adults, and engage in culturally responsive practices that promote equity and inclusion.