Miracle-dz/UniFAR-FTUs huggingface dataset:
A vision-GNN framework for retinopathy classification using optical coherence tomography

Retinopathy refers to pathologies of the retina that can ultimately result in vision impairment and blindness. Optical Coherence Tomography (OCT) is a technique to image these diseases, aiding in the early detection of retinal damage, which may mitigate the risk of vision loss. In this work, we propose an end-to-end Graph Neural Network (GNN) pipeline that can extract deep graph-based features for multi-class retinopathy classification for the first time. To our knowledge, this is also the first work applying Vision-GNN for OCT image analysis. We trained and tested the proposed GNN on a public OCT retina dataset divided into four categories (Normal, Choroidal Neovascularization (CNV), Diabetic Macular Edema (DME), and Drusen). Using our method, we achieve an average accuracy of 99.07% over four classes proving the effectiveness of a deep learning classifier for OCT images with graph-based features. This work lays the foundation to apply GNNs for OCT imaging to aid the early detection of retinal damage.
Longitudinal Examination of Body Mass Index and Cognitive Function in Older Adults: The HELIAD Study

Given the increase in the aging population and thus in the prevalence of dementia, the identification of protective factors against cognitive decline is necessary. In a cohort of 1076 non-demented adults ≥ 65 years old (59.7% women) from the HELIAD study, we assessed whether changes in body mass index (BMI) were associated with changes in cognition over a 3-year follow-up period separately for those ≤ 75 and >75 years old. We identified six BMI trajectory groups based on participants’ BMI status at baseline and at the first follow-up visit; normal to normal BMI was the reference group. Major cognitive domains were evaluated, and a composite index reflecting global cognition was calculated. In participants aged ≤75 years, weight loss—moving from obesity to overweight or normal BMI—was associated with less decline in the memory composite score over time (β = 0.141; p = 0.035), while 3-year maintenance of a BMI ≥ 25 kg/m2 was related to greater reduction in the visuospatial composite score over time (β = −0.093; p = 0.020). Regarding participants aged >75 years, 3-year maintenance of a BMI ≥ 30 kg/m2 contributed to a slower rate of decline in the memory composite score over time (β = 0.102; p = 0.042), whereas weight loss—from overweight to normal BMI—was associated with a decreased attention/processing speed composite score longitudinally (β = −0.275; p = 0.043). Our findings indicated that the association between changes in BMI and cognitive functioning was modified by age. Weight management may have the potential to delay cognitive decline in older adults.
Shape-based segmentation of retinal layers and fluids in OCT image data

The main application of optical coherence tomography (OCT) is in the field of ophthalmology, where it is used for diagnosis of various eye diseases. The automatic segmentation of the individual retinal layers as well as pathological structures in OCT scans is helpful for clinical examination and treatment planning. Current methods often do not consider the strict arrangement of retinal layers. Although graph-based methods are suitable for correcting topology errors, their applicability is costly and complex, especially in the presence of pathologies. In this work, a segmentation method is proposed that utilizes additional shape information of the retinal layers to provide improved topology preservation while maintaining simple applicability. For this purpose, a U-Netbased network architecture is extended to a multi-task approach to allow regression of the shape information of the retinal layers in addition to pixel-wise classification. This introduces spatial regularization and allows the generation of plausible segmentations. A consistency term ensures agreement between the classification and regression task, which also allows for semi-supervised training. In a comprehensive evaluation, the performance of the proposed multi-task approach is investigated, using OCT image data from patients with diabetic macular edema. The results demonstrate that the integration of shape information improves the preservation of the topology of the retinal layers. Moreover, the use of a semi-supervised training scheme via a consistency term improves the robustness and refines the fluid delineation of the proposed method.
Third-order least squares modelling of milling state term for improved computation of stability boundaries

Abstract The general least squares model for milling process state term is presented. A discrete map for milling stability analysis that is based on the third-order case of the presented general least squares milling state term model is first studied and compared with its third-order counterpart that is based on the interpolation theory. Both numerical rate of convergence and chatter stability results of the two maps are compared using the single degree of freedom (1DOF) milling model. The numerical rate of convergence of the presented third-order model is also studied using the two degree of freedom (2DOF) milling process model. Comparison gave that stability results from the two maps agree closely but the presented map demonstrated reduction in number of needed calculations leading to about 30% savings in computational time (CT). It is seen in earlier works that accuracy of milling stability analysis using the full-discretization method rises from first-order theory to second-order theory and continues to rise to the third-order theory. The present work confirms this trend. In conclusion, the method presented in this work will enable fast and accurate computation of stability diagrams for use by machinists.
Misinformation and the Paradox of Trust during the covid-19 pandemic in the U.S.: pathways to Risk perception and compliance behaviors

Abstract As declared “infodemic” by the World Health Organization, the proliferation of Covid-19 misinformation has posed a significant challenge to public health efforts to tackle the pandemic. Despite initial evidence on the association between misinformation and behavior, researchers have yet to fully identify intervening variables to account for the behavioral effects of Covid-19 misinformation. To address this question, this study aims to examine whether and how consuming misinformation would predict public trust in health and political institutions, and in turn, shape risk perception and adherence to preventive behaviors. We conducted a web-based survey using a nationally representative sample of 1,400 U.S. adults in October 2020. We found that Covid-19 misinformation exposure was linked to lower trust in public health experts but higher trust in government, which led to a decrease in the perceived severity of Covid-19 and less compliance with public health guidance. Our findings uncover the complex social and psychological processes by which misinformation consumption undermines public health efforts during the pandemic crisis.
Complementary pseudolabel based on global-and-channel information for unsupervised person reidentification

Abstract. Unsupervised person reidentification (re-ID) is designed to deal with the problem that in industrial application scenarios, the consistent features of the same person cannot be fully mined due to the lack of annotated information in the collected image resources. Researchers focus on using pseudolabels to label images with the same clustering attributes, but traditional clustering methods are prone to generating noisy pseudolabels, which greatly reduce the accuracy of unsupervised person re-ID. We propose a method to refine pseudolabels by generating discriminative information based on channel partitioning. In the design process, the feature map is divided from the channel level, generating a proximity matrix by calculating the distance between the channel feature and the global feature, and select the most negative sample as the verification label to optimize the pseudolabel of the global feature. As a bidirectional guide, the global pseudolabel can be used to further smooth each channel label, keeping the consistency of channel features and global features in pseudolabel selection. This unique method of learning local features can link global information and channel information. Experimental results demonstrate the superior performance of our proposal on representative person re-ID datasets.
The Cannabinoids, CBDA and THCA, Rescue Memory Deficits and Reduce Amyloid-Beta and Tau Pathology in an Alzheimer’s Disease-like Mouse Model

Most studies related to hemp are focused on Cannabidiol (CBD) and Tetrahydrocannabinol (THC); however, up to 120 types of phytocannabinoids are present in hemp. Hemp leaves contain large amounts of Cannabidiolic acid (CBDA) and Tetrahydrocannabinolic acid (THCA), which are acidic variants of CBD and THC and account for the largest proportion of CBDA. In recent studies, CBDA exhibited anti-hyperalgesia and anti-inflammatory effects. THCA also showed anti-inflammatory and neuroprotective effects that may be beneficial for treating neurodegenerative diseases. CBDA and THCA can penetrate the blood–brain barrier (BBB) and affect the central nervous system. The purpose of this study was to determine whether CBDA and THCA ameliorate Alzheimer’s disease (AD)-like features in vitro and in vivo. The effect of CBDA and THCA was evaluated in the Aβ1–42-treated mouse model. We observed that Aβ1–42-treated mice had more hippocampal Aβ and p-tau levels, pathological markers of AD, and loss of cognitive function compared with PBS-treated mice. However, CBDA- and THCA-treated mice showed decreased hippocampal Aβ and p-tau and superior cognitive function compared with Aβ1–42-treated mice. In addition, CBDA and THCA lowered Aβ and p-tau levels, alleviated calcium dyshomeostasis, and exhibited neuroprotective effects in primary neurons. Our results suggest that CBDA and THCA have anti-AD effects and mitigate memory loss and resilience to increased hippocampal Ca2+, Aβ, and p-tau levels. Together, CBDA and THCA may be useful therapeutic agents for treating AD.
Directional Repair in Evolutionary Optimization of m-Objective k-Knapsack Problems

Abstract To solve m-objective k-knapsack problems (mk-KPs) by using evolutionary algorithms, we propose a repair method that transforms infeasible solutions into feasible ones. In evolutionary multi-objective optimization, each solution in the population has a role in approximating a part of the Pareto front. However, since the conventional weighted scalar repair method (WSR) does not consider the position of each solution in the objective space, the solution diversity to approximate a wide range of the Pareto front is deteriorated. To improve the search performance of evolutionary algorithms for solving mk-KPs by enhancing the diversity of solutions, we propose a repair method considering the positions and repair directions of infeasible solutions in the objective space. Experimental results show that the proposed method improves the diversity of solutions and achieves higher search performance than the conventional WSR in mk-KPs.
The interplay of carbon and nitrogen distribution: Prospects for improved crop yields

Growth and productivity of plants primarily depend on the balanced distribution of carbon (C) and nitrogen (N) among different organs. Previous studies on crop improvement have focussed on the C or N assimilation and distribution. However, recent findings reveal that C and N form a complex integrated network and are often dependent on each other to affect crop productivity. The underlying physiological and molecular mechanisms involved in the coordinated distribution of C and N among different plant organs are yet to be fully uncovered. Crucial roles in regulating C and N balance are played by transporters that mediate their movement across different organs. In Cotton, which has an indeterminate growth pattern, source–sink assimilate distribution could be a major bottleneck impeding fibre productivity. This review summarises our current understanding of C and N transport mechanisms, explores and compares different physiological and molecular approaches involved in the C–N distribution cascade, including cotton and other plant species. A comprehensive understanding of these integrated regulatory mechanisms is crucial for improving crop yields and fibre productivity.
Blast-Induced Neurotrauma Results in Spatially Distinct Gray Matter Alteration Alongside Hormonal Alteration: A Preliminary Investigation

Blast-induced neurotrauma (BINT) frequently occurs during military training and deployment and has been linked to long-term neuropsychological and neurocognitive changes, and changes in brain structure. As military personnel experience frequent exposures to stress, BINT may negatively influence stress coping abilities. This study aimed to determine the effects of BINT on gray matter volume and hormonal alteration. Participants were Canadian Armed Forces personnel and veterans with a history of BINT (n = 12), and first responder controls (n = 8), recruited due to their characteristic occupational stress professions. Whole saliva was collected via passive drool on the morning of testing and analyzed for testosterone (pg/mL), cortisol (μg/dL), and testosterone/cortisol (T/C) ratio. Voxel-based morphometry was performed to compare gray matter (GM) volume, alongside measurement of cortical thickness and subcortical volumes. Saliva analyses revealed distinct alterations following BINT, with significantly elevated testosterone and T/C ratio. Widespread and largely symmetric loci of reduced GM were found specific to BINT, particularly in the temporal gyrus, precuneus, and thalamus. These findings suggest that BINT affects hypothalamic–pituitary–adrenal and –gonadal axis function, and causes anatomically-specific GM loss, which were not observed in a comparator group with similar occupational stressors. These findings support BINT as a unique injury with distinct structural and endocrine consequences.
Interpreting the Palu’e Legend Pio Pikariwu

ABSTRACT This article explores the Palu’e legend Pio pikariwu and how the main character Pio is contested by two traditionally rival politico-ceremonial domains on Palu’e island. The stateless clan-structured societies of eastern Indonesia, such as the Palu’e, are not known to have stranger-king myths, the weight of the analysis therefore lies on whether Pio pikariwu fits this category. The relevant themes are compared with the established stranger-king tropes, while basic conceptual tools of comparative Austronesian ethnology, such as that origin establishes precedence, are used to explain the significance of the legend in contemporary society. The legend concurs with several themes of the stranger-king myth, despite that Pio neither becomes a sovereign or has a genealogy. The stranger theme concurs more with a divine kingship related to the South Sulawesi origin histories of founding rulers and a horizontal cultural transfer is plausible due to geographic adjacency and historical connections. A local character projection is also considered. The real-life contestation for Pio involves spiritually potent material heritage and exemplifies how tradition creates new events based on an original event believed to have happened, and that precedence issues extend beyond the domain and its alliances. The demonstrated inherent ambiguity reveals an arrested (divine) stranger-king myth, representing the rejection of kingship.
Molecular Dynamics Simulations Reveal the Conformational Transition of GH33 Sialidases

Sialidases are increasingly used in the production of sialyloligosaccharides, a significant component of human milk oligosaccharides. Elucidating the catalytic mechanism of sialidases is critical for the rational design of better biocatalysts, thereby facilitating the industrial production of sialyloligosaccharides. Through comparative all-atom molecular dynamics simulations, we investigated the structural dynamics of sialidases in Glycoside Hydrolase family 33 (GH33). Interestingly, several sialidases displayed significant conformational transition and formed a new cleft in the simulations. The new cleft was adjacent to the innate active site of the enzyme, which serves to accommodate the glycosyl acceptor. Furthermore, the residues involved in the specific interactions with the substrate were evolutionarily conserved in the whole GH33 family, highlighting their key roles in the catalysis of GH33 sialidases. Our results enriched the catalytic mechanism of GH33 sialidases, with potential implications in the rational design of sialidases.
Fast multi-objective surrogate-assisted design of multi-parameter antenna structures through rotational design space reduction

A technique for fast multi-objective optimisation of antennas is introduced. The core of the proposed methodology is a reliable initial estimation of the design space subset that contains a set of Pareto optimal solutions, i.e. those representing the best possible trade-offs between the conflicting objectives (such as the antenna size and its electrical performance parameters). A fast response surface approximation (RSA) surrogate is subsequently constructed in a reduced search space using sampled coarse-discretisation electromagnetic (EM) simulation data. Owing to the authors’ reduction approach, the surrogate model construction is computationally feasible even when the number of antenna parameters is high. The RSA model is optimised using a multi-objective evolutionary algorithm to yield an initial approximation of the Pareto set. The latter is further refined (to obtain its representation at the high-fidelity EM antenna model level). The approach is illustrated using two design cases. A comparison with previously published methods, as well as experimental validation, is also provided.
Efficient Charge Transfer in MAPbI3 QDs/TiO2 Heterojunctions for High-Performance Solar Cells

Methylammonium lead iodide (MAPbI3) perovskite quantum dots (QDs) have become one of the most promising materials for optoelectronics. Understanding the dynamics of the charge transfer from MAPbI3 QDs to the charge transport layer (CTL) is critical for improving the performance of MAPbI3 QD photoelectronic devices. However, there is currently less consensus on this. In this study, we used an ultrafast transient absorption (TA) technique to investigate the dynamics of charge transfer from MAPbI3 QDs to CTL titanium dioxide (TiO2), elucidating the dependence of these kinetics on QD size with an injection rate from 1.6 × 1010 to 4.3 × 1010 s−1. A QD solar cell based on MAPbI3/TiO2 junctions with a high-power conversion efficiency (PCE) of 11.03% was fabricated, indicating its great potential for application in high-performance solar cells.
Fabrication of Polymeric Hydrogels Containing Esomeprazole for Oral Delivery: In Vitro and In Vivo Pharmacokinetic Characterization

Hydrogel is one of the most interesting and excellent candidates for oral drug delivery. The current study focuses on formulation development of hydrogels for controlled oral delivery of esomeprazole. The hydrogels were prepared by solution casting method by dissolving polymers in Polyvinyl alcohol (PVA) solution. Calcium alginate, Hydroxyl propyl methylcellulose (HPMC), acrylic acid and chondroitin sulfate were used in the preparation of hydrogels. Fourier transform infrared (FTIR) analysis showed no incompatibilities between drug and excipients used in the preparation of formulations. The hydrogels were characterized for size and surface morphology. Drug encapsulation efficiency was measured by Ultraviolet-visible (UV-VIS) spectroscopy. In vitro release studies were carried out using dissolution apparatus. The formulated hydrogels were then compared with the marketed product in vivo using rabbits. The result indicates that prepared hydrogels have a uniform size with a porous surface. The esomeprazole encapsulation efficiency of the prepared hydrogels was found to be 83.1 ± 2.16%. The esomeprazole-loaded hydrogel formulations showed optimum and Pharmacopeial acceptable range swelling behavior. The release of esomeprazole is controlled for 24 h (85.43 ± 0.32% in 24 h). The swelling and release of drug results make the prepared hydrogels a potential candidate for the controlled delivery of esomeprazole. The release of the drug from prepared hydrogel followed the super case transport-2 mechanism. The in vivo studies showed that prepared hydrogel formulations showed controlled and prolonged release of esomeprazole as compared to drug solution and marketed product. The formulations were kept for stability studies; there was no significant change observed in physical parameters, i.e., (appearance, color change and grittiness) at 40 °C ± 2/75% ± RH. There was a negligible difference in the drug content observed after the stability study suggested that all the formulations are stable under the given conditions for 60 days. The current study provides a valuable perspective on the controlled release profile of Hydroxyl propyl methylcellulose (HPMC) and calcium alginate-based esomeprazole hydrogels.
X-rays from accretion shocks in classical T Tauri stars: 2D MHD modeling and the role of local absorption

Abstract In classical T Tauri stars (CTTS) strong shocks are formed where the accretion funnel impacts with the denser stellar chromosphere. Although current models of accretion provide a plausible global picture of this process, some fundamental aspects are still unclear: the observed X-ray luminosity in accretion shocks is order of magnitudes lower than predicted; the observed density and temperature structures of the hot post-shock region are puzzling and still unexplained by models. To address these issues we performed 2D MHD simulations describing an accretion stream impacting onto the chromosphere of a CTTS, exploring different configurations and strengths of the magnetic field. From the model results we then synthesized the X-ray emission emerging from the hot post-shock, taking into account the local absorption due to the pre-shock stream and surrounding atmosphere. We find that the different configurations and strengths of the magnetic field profoundly affect the hot post-shock properties. Moreover the emerging X-ray emission strongly depends also on the viewing angle under which accretion is observed. Some of the explored configuration are able to reproduce the observed features of X-ray spectra of CTTS.
CRFTL: Cache Reallocation-Based Page-Level Flash Translation Layer for Smartphones

Because of its outstanding performance, NAND flash memory in the form of embedded flash drives like Universal Flash Storage (UFS) has been widely used in the modern smartphones. As the core software in UFS, the flash translation layer (FTL) is essential to the performance improvement of UFS. To consider all workloads, a balanced and fixed mapping cache allocation scheme is used in the existing FTLs. Confronted with frequent changes of I/O request patterns of applications in smartphones, the existing FTLs with the fixed mapping cache allocation scheme could not perform well. To consider the I/O characteristics of smartphones, a novel cache reallocation-based page-level flash translation layer, called CRFTL, is proposed in this paper. Through analyzing the life cycle of I/O requests of applications in smartphones, CRFTL stores the mapping entries of I/O requests with different life cycles in different mapping cache structures. CRFTL can be aware of the inner states of NAND flash memory and I/O request patterns, and adaptively adjusts the cache space allocation of the mapping cache structures by applying the approach combining the heuristics and reinforcement learning algorithms. The experimental results show that the performance of our proposed CRFTL is better than that of existing FTLs.