Miracle-dz/UniFAR-FTUs huggingface dataset:
Rheumatoid Arthritis, Cognitive Impairment, and Neuroimaging Biomarkers: Results from the Mayo Clinic Study of Aging

Background: Observational studies suggested that dementia risk in patients with rheumatoid arthritis (RA) is higher than in the general population. Objective: To examine the associations of RA with cognitive decline and dementia, and neuroimaging biomarkers of aging, Alzheimer’s disease, and vascular pathology in adult participants in the Mayo Clinic Study of Aging (MCSA). Methods: Participants with RA were matched 1:3 on age, sex, education, and baseline cognitive diagnosis to participants without RA. RA cases with MRI were also matched with non-cases with available MRI. All available imaging studies (i.e., amyloid and FDG PET, sMRI, and FLAIR) were included. The study included 104 participants with RA and 312 without RA (mean age (standard deviation, SD) 75.0 (10.4) years, 33% male and average follow-up (SD) 4.2 (3.8) years). Results: Groups were similar in cognitive decline and risk of incident dementia. Among participants with neuroimaging, participants with RA (n = 33) and without RA (n = 98) had similar amyloid burden and neurodegeneration measures, including regions sensitive to aging and dementia, but greater mean white matter hyperintensity volume relative to the total intracranial volume (mean (SD)% : 1.12 (0.57)% versus 0.76 (0.69)% of TIV, p = 0.01), and had higher mean (SD) number of cortical infarctions (0.24 (0.44) versus 0.05 (0.33), p = 0.02). Conclusion: Although cognitive decline and dementia risk were similar in participants with and without RA, participants with RA had more abnormal cerebrovascular pathology on neuroimaging. Future studies should examine the mechanisms underlying these changes and potential implications for prognostication and prevention of cognitive decline in RA.
Correlation between Carotid Artery Mean Intima-Media Thickness and brain Natriuretic Peptide in Patients with Psoriasis

Background Numerous studies have documented an association between psoriasis and subclinical atherosclerosis. Objective We aimed to investigate the effects of psoriasis on the levels of N-terminal prohormone B type natriuretic peptide (NT-proBNP) and clarify whether this factor correlates with the evaluations of subclinical atherosclerosis, measured with mean intima-media thickness (MIMT) of the carotid artery. Methods Sixty-one psoriatic patients and sixty-one healthy, age and sex-matched volunteers were enrolled. MIMT was assessed via ultrasonography and serum NT-proBNP level were measured by electrochemiluminescence. Results Both NT-proBNP and MIMT were significantly higher in psoriasis patients. This remained true even after controlling for the effects of age and gender. MIMT was positively correlated with age and serum NT-proBNP level in both groups. Conclusions In conclusion, NT-proBNP levels may be used as a predictor of subclinical atherosclerosis in patients with psoriasis.
Substrate Integrated Waveguide - Dielectric Resonator Antenna for Future Wireless Communication

— A substrate integrated waveguide (SIW) fed rectangular-shaped dielectric resonator antenna (DRA) is proposed for application in the D - Band frequency regime. The Dielectric Resonator (DR) is designed to operate in higher order modes. A rectangular-shaped narrow slot is used to excite the DR element, leading to multiple resonances. The resulting structure is a multi-resonant antenna operating at six frequencies at 123.64 GHz, 125.76 GHz, 127.4 GHz, 129.9 GHz, 134.9 GHz and 137.7 GHz frequencies. These are identified as TE 10δ1 , TE 7δ5 , TE δ31 , TE δ25, TE δ31 and TE δ35 modes of operation of the resonator, respectively. These resonances are merged to achieve a -10 dB impedance bandwidth ranging between 122.58 GHz and 139.51 GHz, equivalent to 13.4% at a center frequency of 125.76 GHz. Moreover, the antenna possesses a stable radiation pattern in the broadband direction, across the entire frequency band of operation. Simulation results show that the antenna has a peak gain of 12.3 dBi, maximum directivity of 13.14 dBi and maximum efficiency of 84% at a frequency of 126 GHz. Simulated results show that the proposed design has potential and suitability for utilization in future wireless communications.
Two Ascophyllum nodosum Fucoidans with Different Molecular Weights Inhibit Inflammation via Blocking of TLR/NF-κB Signaling Pathway Discriminately

The present study aimed to clarify the potential mechanism of fucoidans found in Ascophyllum nodosum on anti-inflammation and to further explore the relationship between their structures and anti-inflammation. Two novel fucoidans named ANP-6 and ANP-7 and found in A. nodosum, were separated and purified and their structures were elucidated by HPGPC, HPLC, GC-MS, FT-IR, NMR, and by the Congo red test. They both possessed a backbone constructed of →2)-α-L-Fucp4S-(1→, →3)-α-L-Fucp2S4S-(1→, →6)-β-D-Galp-(1→, and →3,6)-β-D-Galp4S-(1→ with branches of →2)-α-L-Fucp4S-(1→ and →3)-β-D-Galp-(1→. Moreover, ANP-6 and ANP-7 could prevent the inflammation of the LPS-stimulated macrophages by suppressing the NO production and by regulating the expressions of iNOS, COX-2, TNF-α, IL-1β, IL-6, and IL-10. Their inhibitory effects on the TLR-2 and TLR-4 levels suggest that they inhibit the inflammation process via the blocking of the TLR/NF-κB signal transduction. In addition, ANP-6, with a molecular weight (63.2 kDa), exhibited stronger anti-inflammatory capabilities than ANP-7 (124.5 kDa), thereby indicating that the molecular weight has an influence on the anti-inflammatory effects of fucoidans.
New findings on ligand series used as SARS-CoV-2 virus inhibitors within the frameworks of molecular docking, molecular quantum similarity and chemical reactivity indices

Background: The severe acute respiratory syndrome coronavirus (SARS-CoV)-2 virus causes an infectious illness named coronavirus disease 2019 (COVID-19). SARS-CoV is a positive-sense single-stranded RNA virus from the Betacoronavirus genus. The SARS-CoV-2 RNA-dependent RNA polymerase (RdRp) has an important role in the viral life cycle and its active site is a very accessible region, thus a potential therapeutic approach may be to target this region to study the inhibition of viral replication. Various preexisting drugs have been proposed for the treatment of COVID-19 and the use of existing antiviral agents may reduce the time and cost of new drug discoveries, but the efficacy of these drugs is limited. Therefore, the aim of the present study was to evaluate a number of ligands used as SARS-CoV-2 virus inhibitors to determine the suitability of them for potential COVID-19 treatment. Methods: In this study, we selected a series of ligands used as SARS-CoV-2 virus inhibitors such as: abacavir, acyclovir, amprenavir, ascorbic acid vitamin C, azithromycin, baloxavir, boceprevir, cholecalciferol vitamin D, cidofovir, edoxudine, emtricitabine, hydroxychloroquine and remdesivir. These ligands were analyzed using molecular docking, molecular quantum similarity, and chemical reactivity indices defined within a conceptual density functional theory framework. Results: The analysis of molecular quantum similarity indices on inhibitors showed a high number of differences from a structural point of view. However, they are quite similar in their electronic density, obtaining the highest values in the electronic similarity index. Global and local chemical reactivity indices were analyzed. Conclusions: These studies allowed for the identification of the main stabilizing interactions using the crystal structure of SARS-CoV-2 RdRp. The molecular quantum similarity and chemical reactivity descriptors provide novel insights into these ligands that can be used in the design of new COVID-19 treatments.
Mechanism of the Micellar Solubilization of Curcumin by Mixed Surfactants of SDS and Brij35 via NMR Spectroscopy

The micellar solubilization mechanism of curcumin by mixed surfactants of SDS and Brij35 was investigated at the molecular scale by NMR spectroscopy. Through the investigation of the micelle formation process, types and structures of mixed micelles and solubilization sites, the intrinsic factors influencing the solubilization capacity were revealed. For systems with αSDS = 0.5 and 0.2, the obtained molar solubilization ratios (MSRs) are consistent with the MSRideal values. However, for αSDS = 0.8, the solubilization capacity of curcumin is weakened compared to the MSRideal. Furthermore, only one single mixed SDS/Brij35 micelles are formed for αSDS = 0.5 and 0.2. However, for αSDS = 0.8, there are separate SDS-rich and Brij35-rich mixed micelles formed. In addition, NOESY spectra show that the interaction patterns of SDS and Brij35 in mixed micelles are similar for three systems, as are the solubilization sites of curcumin. Therefore, for αSDS = 0.5 and 0.2 with single mixed micelles formed, the solubility of curcumin depends only on the mixed micelle composition, which is almost equal to the surfactant molar ratio. Although curcumin is solubilized in both separate micelles at αSDS = 0.8, a less stable micelle structure may be responsible for the low solubility. This study provides new insights into the investigation and application of mixed micelle solubilization.
Maternal Consumption of Ultra-Processed Foods-Rich Diet and Perinatal Outcomes: A Systematic Review and Meta-Analysis

The consumption of ultra-processed food (UPF)-rich diets represents a potential threat to human health. Considering maternal diet adequacy during pregnancy is a major determinant for perinatal health outcomes, this study aimed to systematically review and meta-analyze studies investigating the association between maternal consumption of a UPF-rich diet and perinatal outcomes. Conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines, five electronic databases and gray literature using Google Scholar and ProQuest Dissertations and Theses Global were searched up to 31 May 2022. No restrictions were applied on language and publication date. Two reviewers independently conducted the study selection and data extraction process. Meta-analysis was conducted according to the random-effects model. In total, 61 studies were included in the systematic review and the overall population comprised 698,803 women from all gestational trimesters. Meta-analysis of cohort studies showed that maternal consumption of UPF-rich diets was associated with an increased risk of gestational diabetes mellitus (odds ratio (OR): 1.48; 95% confidence interval (CI): 1.17, 1.87) and preeclampsia (OR: 1.28; 95% CI: 1.15, 1.42). Neonatal outcomes showed no association. The overall GRADE quality of the evidence for the associations was very low. The findings highlight the need to monitor and reduce UPF consumption, specifically during the gestational period, as a strategy to prevent adverse perinatal outcomes.
Antioxidant and antibacterial activities of red (Hylocereus polyrhizus) and white (Hylocereus undatus) dragon fruits

Dragon fruit belongs to the genus Hylocereus of the Cactaceae family. There are two species that are commonly cultivated; Hylocerues polyrhizus and Hylocereus undatus that have the same red skin but different flesh colours, red and white respectively. Although from the same genus, the phytochemical contents and bioactivities of both fruits may not be the same. This study aims to compare the phytochemical contents, antioxidant and antibacterial activities of H. polyrhizus and H. undatus to help consumers better choose nutritional fruits and to explore potential natural preservatives. The fruit samples were extracted using 50% ethanol and later were subjected to phytochemical, antioxidant and antibacterial assays. The phytochemical contents were determined using Folin Ciolcalteu and aluminium chloride methods for total phenolic and total flavonoid respectively. The antioxidant activity was determined using diphenyl-picryl hydrazine (DPPH) and 2,2-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) assays. Disk diffusion method was performed to evaluate antibacterial activities against two food-borne pathogens, Escherichia coli and Staphylococcus aureus. H. polyrhizus showed to contain significantly higher phenolic content (p<0.05), while H. undatus had significantly higher flavonoid content (p<0.05). Comparison of antioxidant activities in both fruit samples indicated higher activities were observed in H. polyrhizus and both fruit extracts showed inhibition zones against the tested bacteria with H. polyrhizus extract was able to inhibit at lower concentration. The results suggest that H. polyrhizus may have higher bioactivities compared to H. undatus due to the significantly higher phenolic content.
On the equivalence of the Newton-Kantorovich and distorted Born methods

We show that the Newton-Kantorovich and distorted Born methods for the computational solution of the nonlinear inverse scattering problem are equivalent. This was already shown for the discrete matrix case. Here we present an analysis based on the analytic representations of the integral operators. We first briefly review both methods and then show that they are equivalent.
Enacting Leadership Legitimacy under Terms of Limited Formal and Informal Power in a Heterarchical Network of Organizations

Previous research provides important insights into leadership in inter-organizational networks but says little about how organizations can establish themselves as leaders in heterarchical networks where power is decentralized. We draw on a longitudinal single case study of a German banking cooperative and develop a process model explaining how an organization can create and sustain leadership legitimacy under terms of limited formal and informal power in a heterarchical network. The model advances the understanding of leadership legitimacy in organizational networks in different ways and suggests a new understanding of leadership enacted by formal and informal leaders.
Designing Social Care Plans by Grouping Services and Patients in Mixed Cohorts: A Study using Regression versus Neural Nets

Aims: Linking social needs to social classes using different criteria may lead to social services misuse. The paper discusses using ML and Neural Nentwoks (NNs) in linking public services in Scotland in the long term and advocates this can result in a reduction of the services cost connecting resources needed in groups for similar servicesters. Study Design: The work is based on public data from 22 services offered by Public Health Services (PHS) Scotland that break down into 110 years series called factors. Place and Duration of Study: NHSS and Abertay University, Dundee, from 2018 to 2020 Methodology: The paper discusses using ML and Neural Nentwoks (NNs). The paper combines typical regression models with clustering and cross-correlation as complementary constituents to predict the demand. uses Linear Regression (LR), Autoregression (ARMA) and 3 types of backpropagation (BP) Neural Networks (BPNN) to link them under specific conditions. Results: Relationships found were between smoking related healthcare provision, mental health related health serices, and epidemilogical weight in Primary 1(Education) Body Mass Index (BMI) in chlildren. Primary component analysis (PCA) found 11 significant factors while C-Means (CM) clustering gave 5 major factors clusters. Conclusion: Insurance companies and public policymakers can pack linked services such as those offered to the elderly or to low-income people in the longer term.
Early Learning in Autism as an Atypical Balance between Assimilation and Accommodation Processes

Many children on the autism spectrum are capable of learning large amounts of material in specific areas – yet, they often show learning delays across multiple domains. Additionally, they typically show the ability and motivation to learn from practice and from the outcomes of their own actions, while having difficulties learning from novel situations and from others’ actions and communications. We propose that these and other phenomena reflect, in part, an atypical balance between cognitive assimilation and accommodation processes during early childhood. Adopting a constructivist perspective that connects Piaget’s heuristics with experimental and clinical research in autism, we examine empirical supports as well as implications of this notion for autism research, advocacy, and intervention.
Decision-Making Application of the Cloud-Fog Hybrid Model Based on the Improved Convolutional Neural Network in Financial Services in Smart Medical Care

In order to alleviate the “difficulty in seeing a doctor” for the masses, continuously optimize the service process, and explore new financial service processes for admission and discharge, this study proposes a cloud-fog hybrid model UCNN-BN based on an improved convolutional neural network and applies it to financial services in smart medical care. Decision-making applications: this research improves and designs the UCNN network based on AlexNet and introduces small convolution layers to form convolution groups, making the network more adjustable. The network structure is simpler and more flexible, and it is easy to adjust the algorithm. The number of parameters is small, and it can be directly superimposed without having to add new network hidden layers. The experimental results show that the recognition rate of the UCNN network on the FER2013 and CK+ datasets is higher than that of other recognition methods, and the recognition rates on the FER2013 and CK+ datasets are 98% and 68.01%, due to other methods. This shows that the improved convolutional neural network used in this study for financial services in smart medical care has certain applicability, and small convolution kernels help to extract more subtle features, so as to identify more accurately.
Kinship and Breeding Site Philopatry Drive Fine-Scale Genetic Structure in Fragmented Populations of the Gopher Frog (Rana capito) in North Carolina

Abstract. The Gopher Frog (Rana capito) is a threatened species native to the southeastern longleaf pine ecosystem. Although once much more widespread across the southeastern United States, they now occur in North Carolina at only a handful of disjunct sites in the Coastal Plain and Sandhills regions of the state. The long-term persistence of these populations is thus a concern, as is a loss of genetic variation over time. We used mitochondrial and microsatellite markers to better understand the spatial structure of genetic variation and levels of genetic variability across these remaining populations in order to inform conservation and management decisions. Eight unique mitochondrial haplotypes were found, but these were all genetically similar to one another. Levels of genetic diversity based on the microsatellite analyses were similar across populations, but inbreeding coefficients in two populations were significant, suggesting a potential vulnerability to inbreeding depression. All disjunct populations showed significant genetic differentiation, which was not related to geographic distance. Conversely, within populations, the genetic relatedness of individuals between ponds decreased as distance between ponds increased. This kinship pattern is likely driven by strong breeding philopatry (individuals returning to the same ponds across years) and indicates that conservation actions at the scale of <1 km would primarily affect kin groups of Gopher Frogs, whereas conservation actions at scales ‡1.5 km would be needed to capture more distantly related individuals. Management efforts should thus focus on local metapopulation dynamics by maintaining multiple breeding ponds at each location, and by enhancing connectivity between these breeding ponds.
End-to-end optimization of a diffractive optical element and aberration correction for integral imaging

In the integral imaging light field display, the introduction of a diffractive optical element (DOE) can solve the problem of limited depth of field of the traditional lens. However, the strong aberration of the DOE significantly reduces the final display quality. Thus, herein, an end-to-end joint optimization method for optimizing DOE and aberration correction is proposed. The DOE model is established using thickness as the variable, and a deep learning network is built to preprocess the composite image loaded on the display panel. The simulation results show that the peak signal to noise ratio value of the optimized image increases by 8 dB, which confirms that the end-to-end joint optimization method can effectively reduce the aberration problem.
Generation of training data for named entity recognition of artworks

As machine learning techniques are being increasingly employed for text processing tasks, the need for training data has become a major bottleneck for their application. Manual generation of large scale training datasets tailored to each task is a time consuming and expensive process, which necessitates their automated generation. In this work, we turn our attention towards creation of training datasets for named entity recognition (NER) in the context of the cultural heritage domain. NER plays an important role in many natural language processing systems. Most NER systems are typically limited to a few common named entity types, such as person, location, and organization. However, for cultural heritage resources, such as digitized art archives, the recognition of fine-grained entity types such as titles of artworks is of high importance. Current state of the art tools are unable to adequately identify artwork titles due to unavailability of relevant training datasets. We analyse the particular difficulties presented by this domain and motivate the need for quality annotations to train machine learning models for identification of artwork titles. We present a framework with heuristic based approach to create high-quality training data by leveraging existing cultural heritage resources from knowledge bases such as Wikidata. Experimental evaluation shows significant improvement over the baseline for NER performance for artwork titles when models are trained on the dataset generated using our framework.
Accelerating Large-Scale Graph Neural Network Training on Crossbar Diet

Resistive random-access memory (ReRAM)-based manycore architectures enable acceleration of graph neural network (GNN) inference and training. GNNs exhibit characteristics of both DNNs and graph analytics. Hence, GNN training/inferencing on ReRAM-based manycore architectures give rise to both computation and on-chip communication challenges. In this work, we leverage model pruning and efficient graph storage to reduce the computation and communication bottlenecks associated with GNN training on ReRAM-based manycore accelerators. However, traditional pruning techniques are either targeted for inferencing only, or they are not crossbar-aware. In this work, we propose a GNN pruning technique called DietGNN. DietGNN is a crossbar-aware pruning technique that achieves high accuracy training and enables energy, area, and storage efficient computing on ReRAM-based manycore platforms. The DietGNN pruned model can be trained from scratch without any noticeable accuracy loss. Our experimental results show that when mapped on to a ReRAM-based manycore architecture, DietGNN can reduce the number of crossbars by over 90% and accelerate GNN training by ${\sim }{2}. {7}{\times }$ compared to its unpruned counterpart. In addition, DietGNN reduces energy consumption by more than ${\sim }{3}. {5}{\times }$ compared to the unpruned counterpart.