bluuebunny/crossref_metadata_2025_split huggingface dataset:
Estimation and implications of the genetic architecture of fasting and non-fasting blood glucose
Authors: Qiao Zhen , Sidorenko Julia , Revez Joana A. , Xue Angli , Lu Xueling , Pärna Katri , Snieder Harold ,   Lifelines Cohort Study, Visscher Peter M. , Wray Naomi R. , Yengo Loic 
Published: January 2023

AbstractThe genetic regulation of post-prandial glucose levels is poorly understood. Here, we characterise the genetic architecture of blood glucose variably measured within 0 and 24 h of fasting in 368,000 European ancestry participants of the UK Biobank. We found a near-linear increase in the heritability of non-fasting glucose levels over time, which plateaus to its fasting state value after 5 h post meal (h2 = 11%; standard error: 1%). The genetic correlation between different fasting times is > 0.77, suggesting that the genetic control of glucose is largely constant across fasting durations. Accounting for heritability differences between fasting times leads to a ~16% improvement in the discovery of genetic variants associated with glucose. Newly detected variants improve the prediction of fasting glucose and type 2 diabetes in independent samples. Finally, we meta-analysed summary statistics from genome-wide association studies of random and fasting glucose (N = 518,615) and identified 156 independent SNPs explaining 3% of fasting glucose variance. Altogether, our study demonstrates the utility of random glucose measures to improve the discovery of genetic variants associated with glucose homeostasis, even in fasting conditions.
Gold nanoparticle-enhanced X-ray microtomography of the rodent reveals region-specific cerebrospinal fluid circulation in the brain
Authors: Pan Shelei , Yang Peter H. , DeFreitas Dakota , Ramagiri Sruthi , Bayguinov Peter O. , Hacker Carl D. , Snyder Abraham Z. , Wilborn Jackson , Huang Hengbo , Koller Gretchen M. , Raval Dhvanii K. , Halupnik Grace L. , Sviben Sanja , Achilefu Samuel , Tang Rui , Haller Gabriel , Quirk James D. , Fitzpatrick James A. J. , Esakky Prabagaran , Strahle Jennifer M. 
Published: January 2023

AbstractCerebrospinal fluid (CSF) is essential for the development and function of the central nervous system (CNS). However, the brain and its interstitium have largely been thought of as a single entity through which CSF circulates, and it is not known whether specific cell populations within the CNS preferentially interact with the CSF. Here, we develop a technique for CSF tracking, gold nanoparticle-enhanced X-ray microtomography, to achieve micrometer-scale resolution visualization of CSF circulation patterns during development. Using this method and subsequent histological analysis in rodents, we identify previously uncharacterized CSF pathways from the subarachnoid space (particularly the basal cisterns) that mediate CSF-parenchymal interactions involving 24 functional-anatomic cell groupings in the brain and spinal cord. CSF distribution to these areas is largely restricted to early development and is altered in posthemorrhagic hydrocephalus. Our study also presents particle size-dependent CSF circulation patterns through the CNS including interaction between neurons and small CSF tracers, but not large CSF tracers. These findings have implications for understanding the biological basis of normal brain development and the pathogenesis of a broad range of disease states, including hydrocephalus.
Inversions maintain differences between migratory phenotypes of a songbird
Authors: Lundberg Max , Mackintosh Alexander , Petri Anna , Bensch Staffan 
Published: January 2023

AbstractStructural rearrangements have been shown to be important in local adaptation and speciation, but have been difficult to reliably identify and characterize in non-model species. Here we combine long reads, linked reads and optical mapping to characterize three divergent chromosome regions in the willow warbler Phylloscopus trochilus, of which two are associated with differences in migration and one with an environmental gradient. We show that there are inversions (0.4–13 Mb) in each of the regions and that the divergence times between inverted and non-inverted haplotypes are similar across the regions (~1.2 Myrs), which is compatible with a scenario where inversions arose in either of two allopatric populations that subsequently hybridized. The improved genomes allow us to detect additional functional differences in the divergent regions, providing candidate genes for migration and adaptations to environmental gradients.
Anxiety and functional impairment affects undergraduate psychology students’ learning in remote emergency teaching during the COVID-19 pandemic
Authors: de Sá Vitor Rabelo , Gulá Paula Victoria Sozza Silva , Prizon Tamiris , Falcoski Raquel Messi , Ruggiero Rafael Naime , Padovan-Neto Fernando E. 
Published: January 2023

AbstractThis study aimed to explore the impact of anxiety and functional impairment measures on a sample of undergraduate psychology students. Learning performance was evaluated during the emergency remote teaching during the first wave and in the post-vaccination period of the COVID-19 pandemic in Brazil. Data modeling revealed that psychometric indicators of severe anxiety and severe functional impairment predicted students with lower learning performance in pairs of pre- and post-test multiple-choice questions. This is the first study to highlight the association between measures of generalized anxiety and functional impairment having a deleterious impact on students’ learning performance. This manuscript highlights that educational policies should be designed to deal with students’ mental health under stressful situations.
ACSS2-mediated NF-κB activation promotes alkaliptosis in human pancreatic cancer cells
Authors: Que Dongwen , Kuang Feimei , Kang Rui , Tang Daolin , Liu Jiao 
Published: January 2023

AbstractAlkaliptosis is a recently discovered type of pH-dependent cell death used for tumor therapy. However, its underlying molecular mechanisms and regulatory networks are largely unknown. Here, we report that the acetate-activating enzyme acetyl-CoA short-chain synthase family member 2 (ACSS2) is a positive regulator of alkaliptosis in human pancreatic ductal adenocarcinoma (PDAC) cells. Using qPCR and western blot analysis, we found that the mRNA and protein expression of ACSS2 was upregulated in human PDAC cell lines (PANC1 and MiaPaCa2) in response to the classic alkaliptosis activator JTC801. Consequently, the knockdown of ACSS2 by shRNAs inhibited JTC801-induced cell death in PDAC cells, and was accompanied by an increase in cell clone formation and a decrease in intracellular pH. Mechanically, ACSS2-mediated acetyl-coenzyme A production and subsequent histone acetylation contributed to NF-κB–dependent CA9 downregulation, and this effect was enhanced by the histone deacetylase inhibitor trichostatin A. These findings may provide new insights for understanding the metabolic basis of alkaliptosis and establish a potential strategy for PDAC treatment.
New individualized aflibercept treatment protocol for branch retinal vein occlusion with macular edema
Authors: Noma Hidetaka , Yasuda Kanako , Narimatsu Akitomo , Asakage Masaki , Shimura Masahiko 
Published: January 2023

AbstractWe evaluated the long-term (24-month) efficacy of a novel individualized treatment protocol with 2 mg aflibercept for treatment-naive BRVO with macular edema. Each patient received an initial aflibercept injection and was then examined every 2 weeks until recurrence of edema. At recurrence, each patient received a second injection of aflibercept. The period of efficacy was defined as the time between the first and second injections. Subsequently, each patient was examined and re-injected with aflibercept at their personalized treatment interval, which was defined as 1 week shorter than the period of efficacy. Thirty-seven eyes of 48 patients showed recurrence after the initial injection. The mean period of efficacy was 92.5 ± 40.8 days, and the mean number of visits before recurrence, 7.6 ± 2.9. The mean 24-month best corrected visual acuity (BCVA) was significantly better than the mean baseline BCVA but significantly worse than the best BCVA during the period of efficacy. The mean gain of BCVA at 24 months was 0.07 ± 0.18 logMAR. The mean 24-month central macular thickness (CMT) was significantly lower than the mean baseline CMT but showed no difference from the mean best CMT (p = 0.060). The mean total number of visits during the 24 months was 15.8 ± 3.4. We conclude that the individualized treatment protocol that was based on the period of efficacy in treatment-naïve BRVO eyes with macular edema achieved satisfactory long-term visual outcome.
Runoff prediction of lower Yellow River based on CEEMDAN–LSSVM–GM(1,1) model
Authors: Guo Shaolei , Wen Yihao , Zhang Xianqi , Chen Haiyang 
Published: January 2023

AbstractAccurate medium and long-term runoff forecasts play a vital role in guiding the rational exploitation of water resources and improving the overall efficiency of water resources use. Machine learning is becoming a common trend in time series forecasting research. Least squares support vector machine (LSSVM) and grey model (GM(1,1)) have received much attention in predicting rainfall and runoff in the last two years. “Decomposition-forecasting” has become one of the most important methods for forecasting time series data. Complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) decomposition method has powerful advantages in dealing with nonlinear data. Least squares support vector machine (LSSVM) has strong nonlinear fitting ability and good robustness. Gray model (GM(1,1)) can solve the problems of little historical data and low serial integrity and reliability. Based on their respective advantages, a combined CEEMDAN–LSSVM–GM(1,1) model was developed and applied to the runoff prediction of the lower Yellow River. To verify the reliability of the model, the prediction results were compared with the single LSSVM model, the CEEMDAN–LSSVM model and the CEEMDAN–support vector machines (SVM)–GM(1,1). The results show that the combined CEEMDAN–LSSVM–GM(1,1) model has a high accuracy and the prediction results are better than other models, which provides an effective prediction method for regional medium and long-term runoff prediction and has good application prospects.
Machine learning enabled orthogonal camera goniometry for accurate and robust contact angle measurements
Authors: Kabir Hossein , Garg Nishant 
Published: January 2023

AbstractCharacterization of surface wettability plays an integral role in physical, chemical, and biological processes. However, the conventional fitting algorithms are not suitable for accurate estimation of wetting properties, especially on hydrophilic surfaces, due to optical distortions triggered by changes in the focal length of the moving drops. Therefore, here we present an original setup coupled with Convolutional Neural Networks (CNN) for estimation of Contact Angle (CA). The developed algorithm is trained on 3375 ground truth images (at different front-lit illuminations), less sensitive to the edges of the drops, and retains its stability for images that are synthetically blurred with higher Gaussian Blurring (GB) values (GB: 0–22) if compared to existing goniometers (GB: 0–12). Besides, the proposed technique can precisely analyze drops of various colors and chemistries on different surfaces. Finally, our automated orthogonal camera goniometer has a significantly lower average standard deviation (6.7° vs. 14.6°) and coefficient of variation (14.9 vs. 29.2%) than the existing techniques and enables wettability assessment of non-spherical drops on heterogeneous surfaces.
Pulse distortion caused by waveguide inhomogeneity in nonlinear optical wavelength converters
Authors: Paygan Danial , Izadi Mohammad Amin , Mousavi S. Faezeh , Nouroozi Rahman 
Published: January 2023

AbstractLow-noise integrated all-optical wavelength converters that can be operated in short pulse regime are essential tools to overcome contention resolution in a modern communication network, based on wavelength division multiplexing. Any imperfect functionality in such devices causes non-ideal optical power transfer to the converted data pulses. All imperfections during the preparation and operation of the wavelength converters can be addressed to the waveguide inhomogeneity which distorts data pulses to be converted. This paper reports different waveguide inhomogeneity effects on the pulse distortion while using periodically poled lithium niobate waveguide as wavelength converters. Three types of $$\chi ^{(2)}$$

χ

(
2
)

-based nonlinear optical processes, including second harmonic generation, difference frequency generation, and cascaded second harmonic generation/difference frequency generation are numerically studied to show that any constant, linear, and quadratic waveguide inhomogeneity causes short pulse (down to 1 ns) distortion in such wavelength converters. In addition, it is shown that the reconstruction of $$\textrm{sech}^2$$

sech
2

-shaped generated pulses is possible, when suitable upside-down quadratic variations of obtained inhomogeneity are deliberately induced in the waveguide. Notably, for pulsed second harmonic generation, the generated pulse can be compressed using an upside-down quadratic phase mismatch.
Apparent diameter and cell density of yeast strains with different ploidy
Authors: Fukuda Nobuo 
Published: January 2023

AbstractOptical density at 600 nm (OD600) measurements are routinely and quickly taken to estimate cell density in cultivation and to track cell growth. The yeast Saccharomyces cerevisiae is one of the microorganisms most used in industry, and the OD600 values are frequently adopted as the indicator of yeast cell density, according to the Beer–Lambert law. Because the OD600 value is based on turbidity measurement, the Beer–Lambert law can be applied only for microbial cultivation with low cell densities. The proportionality constants strongly depend on several parameters such as cell size. Typically, yeast strains are categorized into haploids and diploids. It is well known that cell size of diploid yeasts is larger than haploid cells. Additionally, polyploid (especially triploid and tetraploid) yeast cells are also employed in several human-activities such as bread-making and lager-brewing. As a matter of fact, there is almost no attention paid to the difference in the proportionality constants depending on the yeast ploidy. This study presents information for cell size of haploid, diploid, triploid, and tetraploid yeasts with isogenic background, and describes their proportionality constants (k) corresponding to the molar extinction coefficient (ε) in the Beer–Lambert law. Importantly, it was found that the constants are inversely proportional to apparent cell diameters estimated by flow cytometric analysis. Although each cell property highly depends on genetic and environmental factors, a set of results obtained from yeast strains with different ploidy in the current study would serve as a major reference source for researchers and technical experts.
Direct lysis of 3D cell cultures for RT-qPCR gene expression quantification
Authors: Gysens Fien , Ostyn Lisa , Goeteyn Ellen , Blondeel Eva , Nuyttens Justine , De Wever Olivier , de Bony Eric , Crabbé Aurélie , Mestdagh Pieter 
Published: January 2023

AbstractIn vitro cell culture experiments are widely used to study cellular behavior in most biological research fields. Except for suspension cells, most human cell types are cultured as adherent monolayers on a plastic surface. While technically convenient, monolayer cultures can suffer from limitations in terms of physiological relevance, as their resemblance to complex in vivo tissue structures is limited. To address these limitations, three-dimensional (3D) cell culture systems have gained increased interest as they mimic key structural and functional properties of their in vivo tissue counterparts. Nevertheless, protocols established on monolayer cell cultures may require adjustments if they are to be applied to 3D cell cultures. As gene expression quantification is an essential part of many in vitro experiments, we evaluated and optimized a direct cell lysis, reverse transcription and qPCR protocol applicable for 3D cell cultures. The newly developed protocol wherein gene expression is determined directly from crude cell lysates showed improved cell lysis compared to the standard protocol, accurate gene expression quantification, hereby avoiding time-consuming cell harvesting and RNA extraction.
Determination of trace amount of iron cations using electrochemical methods at N, S doped GQD modified electrode
Authors: Kalhori S. , Ahour F. , Aurang P. 
Published: January 2023

AbstractIn this work, nitrogen and sulfur co-doped graphene quantum dot-modified glassy carbon electrodes (N, S-GQD/GCE) were used for the recognition of iron cations in aqueous solutions. The dissolved cations are detected based on the faradaic reduction or oxidation current of Fe(III) and Fe(II) obtained at the N, S-GQD/GCE surface. Cyclic voltammetry (CV), square wave voltammetry (SWV), and hydrodynamic amperometry are used as suitable electrochemical techniques for studying electrochemical behavior and determination of Fe cations. Based on the obtained results, it is concluded that the presence of free electrons in the structure of N, S-GQD could facilitate electron transfer reaction between Fe(III) and electrode surface which with increased surface area results in increased sensitivity and lower limit of detection. By performing suitable experiments, the best condition for preparing the modified electrode and determining Fe(III) was selected. Under optimized conditions, the amperometric response is linear from 1 to 100 nM of Fe(III) with a detection limit of 0.23 nM. The validity of the method and applicability of the sensor is successfully tested by the determination of Fe(III) in drug and water real samples. This sensor opened a new platform based on doped nanoparticles for highly sensitive and selective detection of analytes.
Artificial intelligence reveals features associated with breast cancer neoadjuvant chemotherapy responses from multi-stain histopathologic images
Authors: Huang Zhi , Shao Wei , Han Zhi , Alkashash Ahmad Mahmoud , De la Sancha Carlo , Parwani Anil V. , Nitta Hiroaki , Hou Yanjun , Wang Tongxin , Salama Paul , Rizkalla Maher , Zhang Jie , Huang Kun , Li Zaibo 
Published: January 2023

AbstractAdvances in computational algorithms and tools have made the prediction of cancer patient outcomes using computational pathology feasible. However, predicting clinical outcomes from pre-treatment histopathologic images remains a challenging task, limited by the poor understanding of tumor immune micro-environments. In this study, an automatic, accurate, comprehensive, interpretable, and reproducible whole slide image (WSI) feature extraction pipeline known as, IMage-based Pathological REgistration and Segmentation Statistics (IMPRESS), is described. We used both H&E and multiplex IHC (PD-L1, CD8+, and CD163+) images, investigated whether artificial intelligence (AI)-based algorithms using automatic feature extraction methods can predict neoadjuvant chemotherapy (NAC) outcomes in HER2-positive (HER2+) and triple-negative breast cancer (TNBC) patients. Features are derived from tumor immune micro-environment and clinical data and used to train machine learning models to accurately predict the response to NAC in breast cancer patients (HER2+ AUC = 0.8975; TNBC AUC = 0.7674). The results demonstrate that this method outperforms the results trained from features that were manually generated by pathologists. The developed image features and algorithms were further externally validated by independent cohorts, yielding encouraging results, especially for the HER2+ subtype.
Speech/Language Impairment or Specific Learning Disability? Examining the Usage of Educational Categories
Authors: Georgan Wendy C. , Archibald Lisa M. D. , Hogan Tiffany P. 
Published: January 2023

Purpose:
Developmental language disorder (DLD) is a lifelong condition that when impacting educational performance is identified and serviced through U.S. schools as outlined in the Individuals with Disabilities Education Act. A few examples of educational categories that refer to DLD are (a) speech or language impairment (S/LI) and (b) specific learning disability (SLD). In this research note, we aim to examine trends in how these categories are assigned.

Method:
We analyzed publicly available data released by the U.S. Department of Education from six school years between 2010 and 2020. We examined the use of S/LI and SLD categories across students of different ages at the U.S. national and state levels.

Results:
We present a trend in which younger students tend to be identified with the S/LI category, whereas older students tend to be identified with the SLD category. This trend is evident in all 6 years of data analyzed at the national level, and in 49 of 50 states.

Conclusions:
We discuss these findings in the context of research on language disorders to explain this trend. We highlight the potential damaging effects of using inconsistent terminology, including affecting the services for which students with DLD qualify and causing confusion for their parents and educators.
Opportunities and Challenges for Audiology and Speech-Language Pathology Services in Arabic-Speaking Middle East Countries
Authors: Ghali Samantha , Abdalla Fauzia , Aseeri Areej 
Published: January 2023

Purpose:
This article outlines opportunities and challenges pertaining to service delivery faced by audiologists and speech-language pathologists/therapists working in Arabic-speaking Middle Eastern nations. Among the most diverse regions in the world, the Middle East is a region characterized by rapid development in a number of sectors relevant to audiology and speech-language pathology, including education and health care. Despite these developments, there remains a number of challenges, including growing service demands, awareness of the scope of practice of audiologists and speech-language pathologists/therapists, regulation of clinical practice, and shortages of clinicians and evidence-based clinical resources.

Conclusion:
Developments to address these challenges, as well as strategies that may enhance service delivery, will be presented.
Noise‐tolerate and adaptive coefficient zeroing neural network for solving dynamic matrix square root
Authors: Xiao Xiuchun , Jiang Chengze , Mei Qixiang , Zhang Yudong 
Published: January 2023

AbstractThe solving of dynamic matrix square root (DMSR) problems is frequently encountered in many scientific and engineering fields. Although the original zeroing neural network is powerful for solving the DMSR, it cannot vanish the influence of the noise perturbations, and its constant‐coefficient design scheme cannot accelerate the convergence speed. Therefore, a noise‐tolerate and adaptive coefficient zeroing neural network (NTACZNN) is raised to enhance the robust noise immunity performance and accelerate the convergence speed simultaneously. Then, the global convergence and robustness of the proposed NTACZNN are theoretically analysed under an ideal environment and noise‐perturbed circumstances. Furthermore, some illustrative simulation examples are designed and performed in order to substantiate the efficacy and advantage of the NTACZNN for the DMSR problem solution. Compared with some existing ZNNs, the proposed NTACZNN possesses advanced performance in terms of noise tolerance, solution accuracy, and convergence rate.