Miracle-dz/UniFAR-FTUs huggingface dataset:
Decrease in Ocular Blood Flow Thirty Minutes After Intravitreal Injections of Brolucizumab and Aflibercept for Neovascular Age-Related Macular Degeneration

Purpose Intravitreal injections of anti-vascular endothelial growth factor (VEGF) agents such as brolucizumab and aflibercept are used widely to treat neovascular age-related macular degeneration (nAMD); however; they may theoretically affect the ocular blood flow. We investigated the short-term changes in the ocular blood flow between eyes with nAMD treated with intravitreal brolucizumab injections (IVBr) and intravitreal aflibercept injections (IVA). Methods This study included 21 eyes of 21 Japanese patients with nAMD treated with either IVBr or IVA at Kurume University Hospital from April 2021 through June 2022. The rates of ocular blood flow at the optic nerve head (ONH mean blur rate [MBR]-vessel) and at the choroid (CHOR MBR) were analyzed before and 30 minutes after injections using laser speckle flowgraphy. Results In the IVBr-treated group, the ONH MBR-vessel and CHOR MBR rates decreased significantly by 10.6% and 16.9% from baseline to 30 minutes after IVBr, respectively. In the IVA-treated group, ONH MBR-vessel and CHOR MBR rates decreased significantly by 9.4% and 6.1% from baseline to 30 minutes after IVA, respectively. There was no significant difference in the rates of decrease in the ONH MBR-vessel or CHOR MBR between the IVBr-treated and IVA-treated groups. Conclusion Intravitreal injections of brolucizumab and aflibercept in eyes with nAMD cause significant decreases in ocular blood flow at the ONH and the choroid 30 minutes after injection. The rate of decrease in ocular blood flow was not significant between the eyes treated with brolucizumab and aflibercept. However, 3 of 10 eyes treated with brolucizumab but none of 11 eyes treated with aflibercept had more than a 30% decrease in the ocular blood flow at the choroid 30 minutes after injection.
Analysis of a Turbine Flow Meter Calibration Curve using CFD

The primary purpose of this paper was to develop a CFD model of a turbine flow meter. This model was used to fully characterize the calibration of a turbine flow meter by varying flow rate, viscosity, and density of the fluid and obtain a steady state rotor speed as the response. A three-dimensional mesh was created using geometry from a typical flow meter used in fuel transfer applications. This model produced a calibration results that mimics the response seen from experimentation and extensive calibration. This can provide some useful insights that may aid in calibration and design of turbine flow meters.
Coverage and Correlations Between Open Citations in Crossref and Readership in Mendeley: Different Fields of Brazilian Science

Purpose: This study dwells on the coverage of altmetric indicators in the Mendeley reference manager, citations in the Crossref platform, and the correlations between readers and citations of papers by researchers working in Brazil, according to different fields and subfields of knowledge. Design/Methodology/Approach: We analyzed 152,727 scientific papers published between 2017-2018 by researchers working in Brazil. Findings: The coverage of Brazilian papers by Mendeley is relatively high (87.91%) and 63% of Brazilian papers had citations in Crossref. In the Brazilian context, Mendeley readership is relatively high (average R 2 reaching 70% in some areas). Originality/Value: The correlation between readers in Mendeley and citations in Crossref of Brazilian scientific output is between moderate and high. This indicates that Mendeley and Crossref data can be relevant for evaluating Brazilian science. The data reveal that Mendeley readership can, in many fields, predict citations. These data are generally higher and mor homogeneous than those found in the international researches. The scientific communication of international context (based on international journals) and Brazilian (heavily published in national open access journals) and the information sources used in the studies may partly explain the differences between them.
Some new Applications of

We prove a new result in the Theory of Orbit Harmonics and derive from it the Cohen Macauliness of the ring QIm(G )o fm-Quasi-Invariants of a Coxeter Group G. Using the non-degeneracy of the fundamental bilinear form on QIm(G), this approach yields a direct and simple proof that the quotient of QIm(G )b y the ideal generated by the homogeneous G-invariants affords a graded version of the left regular representation of G. These results are a consequence of a combination of work of Etingof-Ginzburg [3], Feigin-Veselov [6] and Felder-Veselov [5]. The arguments here are quite elementary and self contained except for what concerns the non-degeneracy of the fundamental bilinear form.
Leveraging preserved specimens of Nerodia to infer the spatiotemporal dynamics of Ophidiomyces ophidiicola via quantitative polymerase chain reaction

Abstract Ophidiomyces ophidiicola (Oo) is a fungal pathogen and the causative agent of ophidiomycosis that has affected multiple snake taxa across the United States, Europe, and Asia. Ophidiomycosis has often been referred to as an emerging infectious disease (EID); however, its status as an EID has recently come under debate. Oo infections have been confirmed in wild snake populations in Texas; however, it is unknown if the pathogen is novel (i.e., invasive) or endemic to the state. To address this knowledge gap, we conducted surveys for Oo among preserved Nerodia deposited at three university museums in Texas. First, we visually assessed snakes for signs of infection (SOI), and if SOI were present, we sampled the affected area. We then used quantitative polymerase chain reaction to diagnose the presence of Oo DNA on areas with SOI and used these data to evaluate spatiotemporal patterns of Oo prevalence. We also tested for significant spatial clusters of Oo infenction using a Bernoulli probability model as implemented in the program SatScan. We found that the proportion of snakes exhibiting SOI was constant over time while the prevalence of Oo DNA among those SOI increased across space and time. Within these data, we detected an incidence pattern consistent with an introduction and then spread. We detected six spatial clusters of Oo infection, although only one was significant. Our results support the hypothesis that Oo is an emerging, novel pathogen to Texas snakes. These data narrow the knowledge gap regarding the history of Oo infections in Texas and establish a historical record of confirmed Oo detections in several counties across the state. Thus, our results will guide future research to those areas with evidence of past Oo infections but lacking confirmation in contemporary hosts.
Awareness Level Regarding Brain Death and the Acceptance of Organ Donation in Eastern Province, Saudi Arabia

Background There is a worldwide shortage of organ donations. In the United States, 20% of people on transplant waiting lists pass away annually due to the lack of accessible organs. Patients with brain death can donate organs, which may save other patients’ lives. The Saudi Ministry of Health endorses brain death as equivocal to whole-body death. A study conducted in Saudi Arabia showed that there was a mild to moderate level of awareness regarding brain death. This study aimed to investigate the awareness and knowledge level regarding brain death and the acceptance of organ donation among the general population in Eastern Province, Saudi Arabia. Methodology An observational, cross-sectional study was conducted among 1,740 adults using an online questionnaire created and published in February 2023 to collect data from Saudi males and females aged 18 or older who were willing to participate in the study. The data were analyzed using SPSS version 23.0 (IBM Corp., Armonk, NY, USA) after collecting and entering them using the Windows version of Microsoft Office Excel 2016. Results Overall, 85.6% of the study participants had heard about organ donation. Of them, about 42.4% were aware of brain death. Further, 40% of participants were in agreement with organ donation. According to the findings, the majority of participants (60.9%) believed that a person could donate his or her organs during their life, while only 42.6% were unaware that they could donate their organs during death. Only 10.8% of participants knew that blood can be donated. There was no significant association between factors associated with organ donation and gender, education level, or monthly income. Conclusions This study concluded that study participants had a low level of awareness about brain death. Understanding brain death is essential for persuading people to donate their organs. Thus, more has to be done to inform and educate people about brain death and how it affects organ donation.
Increasing participation in pro-poor tourism: study of predictors to promote prosocial behavior to other reference groups

ABSTRACT Pro-Poor Tourism (PPT) is an approach to alleviate absolute poverty and has two perspectives: increasing the profit allocation to the poor and expanding the profit itself. This study focuses on the latter and investigates factors to increase people’s intention to participate in PPT. However, the participating intention was interpreted based on donation intention to absolute poverty, a global prosocial behavior, because PPT has low social recognition. The structural equation modeling suggested that donation intention was affected by Empathic Concern (EC) and Social Norms (SN). However, the donation is an actual behavior, but PPT is a concept. Therefore, the study indicated embodying the concept as products to stimulate tourists’ EC, such as optional tours which distribute preferential benefits to the poor. On the other hand, for social norms to function, the study also indicated that consensus needs to cultivate that PPT effectively alleviates absolute poverty, a global issue.
Advanced 3D Models of Human Brain Tissue Using Neural Cell Lines: State-of-the-Art and Future Prospects

Human-relevant three-dimensional (3D) models of cerebral tissue can be invaluable tools to boost our understanding of the cellular mechanisms underlying brain pathophysiology. Nowadays, the accessibility, isolation and harvesting of human neural cells represents a bottleneck for obtaining reproducible and accurate models and gaining insights in the fields of oncology, neurodegenerative diseases and toxicology. In this scenario, given their low cost, ease of culture and reproducibility, neural cell lines constitute a key tool for developing usable and reliable models of the human brain. Here, we review the most recent advances in 3D constructs laden with neural cell lines, highlighting their advantages and limitations and their possible future applications.
Applying Graph Neural Networks to the Decision Version of Graph Combinatorial Optimization Problems

In recent years, there has been a significant increase in the application of graph neural networks on a wide range of different problems. A specially promising direction of research is on graph convolutional neural networks (GCN). This work focuses on the application of GCNs to graph combinatorial optimization problems (GCOPs), specifically on their decision versions. GCNs are applied on the family of GCOPs in which the objective function is directly related to the number of vertices in a graph. The selected problems are the minimum vertex cover, maximum clique, minimum dominating set and graph coloring. In this work, a framework is proposed for solving problems of this type. The performance of this approach is explored for standard multi-layer GCNs using different types of convolutional layers. On top of this, we propose a new structure that is based on graph isomorphism networks. Computational experiments show that this new structure is significantly more efficient than basic structures. To further improve the performance of this method, the use of GCN ensembles is also explored. When using GCNs to generate solution values for GCOPs, they often correspond to non-feasible solutions because they violate the problem boundaries. This issue is addressed by defining an asymmetric loss function that is used during the GCN training. The proposed method is evaluated on a large set of training data consisting of graph solution pairs that can also be accessed online.
Usefulness of the Visual Cognitive Assessment Test in Detecting Mild Cognitive Impairment in the Community.

BACKGROUND A delay in the detection of mild cognitive impairment (MCI) in the community delays the opportunity for early intervention. Accurate tools to detect MCI in the community are lacking. The Visual Cognitive Assessment Test (VCAT) is a visual based cognitive test useful for multilingual populations without the need for translation. OBJECTIVE Here, we evaluate the usefulness of VCAT in detecting MCI in a community population in Singapore. METHODS We recruited 301 participants from the community who completed a detailed neuropsychological assessment and 170 of them completed a 3T magnetic resonance imaging (MRI) brain scan. We performed a receiver operating characteristics analysis to test the diagnostic performance of VCAT compared to Montreal Cognitive Assessment (MoCA) in distinguishing MCI from cognitively normal (CN) by measuring area under the curve (AUC). To test for the association of VCAT with structural MRI, we performed a Pearson's correlation analysis for VCAT and MRI variables. RESULTS We recruited 39 CN and 262 MCI participants from Dementia Research Centre (Singapore). Mean age of the cohort was 63.64, SD = 9.38, mean education years was 13.59, SD = 3.70 and majority were women (55.8%). VCAT was effective in detecting MCI from CN with an AUC of 0.794 (95% CI 0.723-0.865) which was slightly higher than MoCA 0.699 (95% CI 0.621-0.777). Among subjects with MCI, VCAT was associated with medial temporal lobe atrophy (ρ = -0.265, p = 0.001). CONCLUSIONS The VCAT is useful in detecting MCI in the community in Singapore and may be an effective measure of neurodegeneration.
Regional Economic Growth and Inequality in India during the Pre- and Post-reform Periods

Abstract This paper evaluates the economic performance of 15 major states in India, and examines whether initially disparate states displayed any tendency towards convergence in real per capita income during the period 1960/61–2006/07. Though the growth performance of the states has improved in the post-reform period, since 1991 the states have diverged in per capita income. The states following different steady-state paths are classified into three clubs—one convergent and two non-convergent. The regional divergence and club convergence are explained in terms of interstate variations in physical and social infrastructures, state-level policy reforms, foreign direct investment flows and economic structure. The poorly performing states could improve their relative economic position by undertaking investments in physical and social infrastructures, and speeding up the reform process by liberalizing investment and infrastructure policies. As industry and services are the major sources of regional divergence, any effort to reduce regional imbalance must focus primarily on these two sectors.
Regulation of HAND2 Expression by LncRNA HAND2-AS1 in Ovarian Endometriosis Involving DNA Methylation

Abstract HAND2 is a critical mediator of progesterone receptor signaling in endometrium. Silencing of HAND2 expression is associated with female infertility and endometrial cancers. We recently observed that lncRNA HAND2-AS1 and HAND2 are expressed coordinately in human endometrial stromal cells. To investigate involvement of HAND2-AS1 and HAND2 in pathogenesis of endometriosis, we employed immunohistochemistry, in situ hybridization, and quantitative real-time PCR to assess their expression in normal endometrium and the ectopic lesions obtained from patients with ovarian endometriosis. HAND2 promoter methylation was also monitored in these samples. Our results revealed that HAND2 and HAND2-AS1 expression levels were reduced but promoter methylation was enhanced significantly in ectopic endometrium when compared with the normal controls. Fluorescence in situ hybridization showed that HAND-AS1 is predominantly localized in the nuclei of endometrial stromal cells in contrast to the cytoplasmic distribution in epithelial cell compartment. To further investigate regulation of HAND2 expression by HAND2-AS1, HAND2-AS1 was silenced or overexpressed in human endometrial stromal cells. Our studies showed that expression levels of HAND2 and its direct target IL15 were attenuated markedly in HAND2-AS1 silenced cells but enhanced significantly in the overexpressed human endometrial stromal cells. Silencing of HAND2-AS1 also impaired endometrial stromal cell decidualization as indicated by downregulation of decidual biomarkers IGFBP1 and PRL. In addition, HAND2 promoter methylation was also enhanced upon HAND2-AS1 silencing. RNA immunoprecipitation studies further revealed that HAND2-AS1 is capable of binding to DNA methyltransferase DNMT1, indicating that HAND2-AS1 governs HAND2 expression epigenetically involving DNA methylation.
Recent advances and perspectives in nucleotide second messenger signaling in bacteria

Abstract Nucleotide second messengers act as intracellular ‘secondary’ signals that represent environmental or cellular cues, i.e. the ‘primary’ signals. As such, they are linking sensory input with regulatory output in all living cells. The amazing physiological versatility, the mechanistic diversity of second messenger synthesis, degradation, and action as well as the high level of integration of second messenger pathways and networks in prokaryotes has only recently become apparent. In these networks, specific second messengers play conserved general roles. Thus, (p)ppGpp coordinates growth and survival in response to nutrient availability and various stresses, while c-di-GMP is the nucleotide signaling molecule to orchestrate bacterial adhesion and multicellularity. c-di-AMP links osmotic balance and metabolism and that it does so even in Archaea may suggest a very early evolutionary origin of second messenger signaling. Many of the enzymes that make or break second messengers show complex sensory domain architectures, which allow multisignal integration. The multiplicity of c-di-GMP-related enzymes in many species has led to the discovery that bacterial cells are even able to use the same freely diffusible second messenger in local signaling pathways that can act in parallel without cross-talking. On the other hand, signaling pathways operating with different nucleotides can intersect in elaborate signaling networks. Apart from the small number of common signaling nucleotides that bacteria use for controlling their cellular “business,” diverse nucleotides were recently found to play very specific roles in phage defense. Furthermore, these systems represent the phylogenetic ancestors of cyclic nucleotide-activated immune signaling in eukaryotes.
The Equilibrium Effect of Information in Consumer Credit Markets: Public Records and Credit

Information in credit reports determines the availability and cost of credit and impacts many other areas of consumers’ lives. Yet relatively little is known about what happens to consumers and the market equilibrium when the information contained in credit reports changes. We use the July 2017 nationwide purge of non-bankruptcy public records from credit reports to estimate the individual and equilibrium effects of information removal in consumer credit markets. To guide our analysis, we develop a model that implies that consumers who did not have a public record prior to the purge lost more distinguishing information in the purge if they were in a submarket with a higher share of consumers with a public record. Empirically, we fnd three different effects on the market: (1) Using the exogenous removal of non-bankruptcy public records during the purge, we fnd that the likelihood of having a credit card and an auto loan increased by 2.3 and 1.3 percentage points, respectively, for the 11.8 percent of low-credit-score consumers losing a public record. (2) For these consumers, there was also a 7.9 percent increase in existing credit limits and a 14.7 percent increase in credit card debt, with three quarters of this response being an endogenous dynamic response to credit expansion. (3) Among consumers who did not lose a public record, there was signifcant redistribution of credit from consumers in a submarket with a higher share of consumers with a public record to consumers in a submarket with a lower share.
Event-Triggered Finite-Time Synchronization Control for Quaternion-Valued Memristive Neural Networks by an Non-Decomposition Method

Applying the event-triggered control, this article discusses the finite-time synchronization issue of quaternion-valued memristive neural networks (QVMNNs) with time-varying delays. By employing the improved one norm and sign function of quaternion, the QVMNNs can be analyzed as an entirety without any decomposition. To relieve the communication pressure, a proper event-triggered controller is designed, then the event-triggered conditions and some criteria are also established to guarantee finite-time synchronization. Moreover, the synchronization time is estimated by direct analysis, and the positive lower bound of the inter-event time is obtained to get rid of the Zeno behavior. In addition, according to the acquired event-triggered scheme, a self-triggered scheme is further provided to refrain from continuous detection. Ultimately, the validity of the obtained theoretical results is demonstrated by a numerical simulation.
Mental Illness as a Sentencing Determinant: A Comparative Case Law Analysis Based on a Machine Learning Approach

This study identifies factors that contribute to sentencing outcomes for criminally sentenced individuals experiencing mental disorders, in two U.S. states with divergent sociopolitical ideologies. Recent case law (n = 130) from appellate courts in New York and Kansas (from 2020 to 2021) was analyzed using regression and machine learning to predict sentence severity for individuals experiencing mental disorders. Across both states, trauma-related and personality disorders led to the most severe sentences, while paraphilia, addiction, and mood disorders had the lowest probability of imprisonment. Sentencing outcomes in Kansas were significantly more severe as compared with New York. A classification analysis identified important patterns of sentencing determinants that predicted which mental disorders were more likely to lead to incarceration. Findings and implications are discussed in relation to punishment disparities as well as the potentials and pitfalls regarding the use of machine learning approaches in criminal justice research and policy.
Retinal Disease Detection Using Deep Learning Techniques: A Comprehensive Review

Millions of people are affected by retinal abnormalities worldwide. Early detection and treatment of these abnormalities could arrest further progression, saving multitudes from avoidable blindness. Manual disease detection is time-consuming, tedious and lacks repeatability. There have been efforts to automate ocular disease detection, riding on the successes of the application of Deep Convolutional Neural Networks (DCNNs) and vision transformers (ViTs) for Computer-Aided Diagnosis (CAD). These models have performed well, however, there remain challenges owing to the complex nature of retinal lesions. This work reviews the most common retinal pathologies, provides an overview of prevalent imaging modalities and presents a critical evaluation of current deep-learning research for the detection and grading of glaucoma, diabetic retinopathy, Age-Related Macular Degeneration and multiple retinal diseases. The work concluded that CAD, through deep learning, will increasingly be vital as an assistive technology. As future work, there is a need to explore the potential impact of using ensemble CNN architectures in multiclass, multilabel tasks. Efforts should also be expended on the improvement of model explainability to win the trust of clinicians and patients.