Miracle-dz/UniFAR-FTUs huggingface dataset:
Sex-dependent Pathological Aging Effect on Caudate Functional Connectivity in Mild Cognitive Impairment

Purpose To assess the pathological aging effect on caudate functional connectivity among mild cognitive impairment (MCI) participants and examine whether and how sex and amyloid contribute to this process. Materials and Methods 277 functional magnetic resonance imaging (fMRI) sessions from 163 cognitive normal (CN) older adults and 309 sessions from 139 participants with MCI were included as the main sample in our analysis. Pearson’s correlation was used to characterize the functional connectivity (FC) between caudate and each brain region, then caudate nodal strength was computed to quantify the overall caudate FC strength. Association analysis between caudate nodal strength and age was carried out in MCI and CN separately using linear mixed effect (LME) model with covariates (education, handedness, sex, Apolipoprotein E4 and intra-subject effect). Analysis of covariance was conducted to investigate sex, amyloid status and their interaction effects on aging with the fMRI data subset having amyloid status available. LME model was applied to women and men separately within MCI group to evaluate aging effects on caudate nodal strength and each region’s connectivity with caudate. We then evaluated the roles of sex and amyloid status in the associations of neuropsychological scores with age or caudate nodal strength. An independent cohort was used to validate the sex-dependent aging effects in MCI. Results The MCI group had significantly stronger age-related increase of caudate nodal strength compared to the CN group. Analyzing women and men separately revealed that the aging effect on caudate nodal strength among MCI participants was significant only for women (left: P=6.23x10−7, right: P=3.37x10−8), but not for men (P>0.3 for bilateral caudate). The aging effects on caudate nodal strength were not significantly mediated by brain amyloid burden. Caudate connectivity with ventral prefrontal cortex substantially contributed to the aging effect on caudate nodal strength in women with MCI. Higher caudate nodal strength is significantly related to worse cognitive performance in women but not in men with MCI. Conclusion Sex modulates the pathological aging effects on caudate nodal strength in MCI regardless of amyloid status. Caudate nodal strength may be a sensitive biomarker of pathological aging in women with MCI.
RSS+: Pro-Active Risk Mitigation for AV Safety Layers based on RSS

Addressing safety for future autonomous vehicles (AVs) while maintaining a high practicability, i.e. not using excessive safety margins, getting stuck or not able to drive at all, is still an open research question. In this regard, the Responsibility Sensitive Safety (RSS) approach, which is a formal parametric safety model, is a promising concept and is currently gaining lots of attraction. With RSS it is possible to combine safety and practicability, if the model parameters are chosen in a reasonable manner, i.e. good enough to achieve a desirable level of safety, but not covering the physical worst case. However, as we will show in this work, this can lead to a not optimal behavior of the AV in extreme situations, when other traffic participants violate RSS assumptions. To overcome this, we propose an extension to RSS, called RSS +, which can mitigate many of these situations in a pro-active manner, without sacrificing practicability.
Relationships of Telomere Homeostasis with Oxidative Stress and Cardiac Dysfunction in Human Ischaemic Hearts

Although the roles of telomeres and oxidative stress in ischaemic cardiomyopathy (ICM) are known, mechanisms of telomere homeostasis and their relationship with oxidative stress are incompletely understood. We performed two RNA-seq analyses (mRNA n = 23; ncRNA n = 30) and protein validation on left ventricles of explanted hearts from ICM and control subjects. We observed dysregulation of the shelterin and cohesin complexes, which was related to an increase in the response to cellular oxidative stress. Moreover, we found alterations at mRNA level in the mechanisms of telomeric DNA repair. Specifically, increased RAD51D mRNA levels were correlated with left ventricular diameters. RAD51D protein levels were unaltered, however, and were inversely corelated with the miR-103a-3p upregulation. We also observed the overexpression of lncRNAs (TERRA and GUARDIN) involved in telomere protection in response to stress and alterations in their regulatory molecules. Expression of the TERRA transcription factor ATF7 was correlated with superoxide dismutase 1 expression and left ventricular diameters. The levels of GUARDIN and its transcription factor FOSL2 were correlated with those of catalase. Therefore, we showed specific alterations in the mechanisms of telomeric DNA repair and protection, and these alterations are related to an increase in the response mechanisms to oxidative stress and cardiac dysfunction in ICM.
A Nonlinear Autoregressive Exogenous (NARX) Neural Network Model for the Prediction of Timestamp Influence on Bitcoin Value

The transaction and market of bitcoin is volatile, meaning it’s uncertain because it changes frequently. There have been a number of research studies that have presented bitcoin price prediction models, but none of them have looked at the controlling variables linked with bitcoin transaction timestamps. It might be that price is not the only key criteria influencing bitcoin transactions, or the available model for bitcoin price prediction is yet to consider timestamp as a determining factor in its transaction. A better and more accurate model would be required to predict how the Timestamp influences changes of bitcoin transactions. That is why this current study utilized a Nonlinear Autoregressive Exogenous (NARX) Neural Network Model for the prediction timestamp influence on Bitcoin value. Bitcoin historical datasets which are converted to a nonlinear regression into a "well-formulated" statistical problem in the manner of a ridge regression are used. Simulation analysis indicates that bitcoin digital currency’s performance variation is highly influenced by its transaction timestamp with the prediction accuracy of 96%. The contributions of this research lies with the fact that specific Bitcoin transaction events repeat themselves over and over again, meaning that the Open-Price, High-Price, Low-Price, and Close-Price of Bitcoin price over timestamp developed a pattern that was predicted by NARX with less That means those involved in the transaction of bitcoin at the wrong timestamp will certainly face the uncertainty negative effect of the bitcoin market.
Detection of microparticles in flow by 2D light scattering and fluorescence imaging

The in-flow detection of microparticles is of great importance in biomedical fields. Previous studies have shown that twodimensional (2D) light scattering technology has the capability to detect and differentiate unlabeled cells, while fluorescence imaging can be used as a specific cytological detection method. Here, we built a system to acquire simultaneously the 2D light scattering patterns and fluorescence images of single microparticles in flow. A 488 nm laser beam was reshaped into a light sheet by a cylindrical lens, which was used to excite the microparticles in a microfluidic tube. The 2D light scattering and fluorescence signals were collected by a 10x objective lens through a 505 nm dichroic beam splitter, and they were simultaneously acquired by dual complementary metal oxide semiconductor (CMOS) detectors. Our system acquired the 2D light scattering patterns and fluorescence images of single microparticles from a mixture (2 μm fluorescent particles, 2 μm and 3.87 μm non-fluorescent particles) in flow. Based on the 2D light scattering patterns and the fluorescence images, the number and the ratio of mixed microparticles can be determined. Moreover, the label-free microparticle size differentiation can also be achieved with the 2D light scattering patterns. The detection of microparticles in flow that combines label-free 2D light scattering technology and fluorescence imaging may find potential applications for single cell analysis.
The Influence of Big Data Analytics on E-Commerce: Case Study of the U.S. and China

Big data analytics (BDA) is a wide and deep application in e-commerce, which impacts positively on the global economy, especially the U.S. and China who have done well. This paper seeks to examine the relative influence of theoretical research and practical activities of BDA in e-commerce to explain the differences between the U.S. and China according to the two main literature databases, Web of Science and CNKI, respectively, and by employing other samples that present retail e-commerce sales and the number of some data companies founded in the U.S. and China each year. We further determine the reasons leading to the difference between the U.S. and China in BDA in e-commerce, which can help managers devise appropriate business strategies in e-commerce for each of them, and provide a proof of the significant relationship of theoretical research and practical activities in BDA in e-commerce. In addition, the variables related to big data companies show a moderation effect rather than mediating effect relative to the practice of theoretical research in e-commerce in the United States, but they show a moderate effect and mediating effects in China. The results of this study help clarify doubts regarding the development of China’s e-commerce. Moreover, three orientations in e-commerce using BDA and the use of quantum computing in e-commerce to solve existing e-commerce problems are explored to provide better evidence for decision-making that could be valuable in future research.
Development of green extraction process to produce antioxidant-rich extracts from purple coneflower

ABSTRACT The present study was conducted to develop subcritical water extraction (SWE) of Echinacea purpurea flowers. The influence of temperature and extraction time on quality of extracts considering total phenols content, total flavonoids content, antioxidant capacity and extraction yield, was determined. Optimized extraction parameters for maximised investigated responses were as follows: 147.56 °C and 8.43 min. The experimental values agreed with the values predicted, thus indicating the adequacy of central composite experimental design for modelling the SWE of bioactive compounds from E. purpurea. Results of the study also highlighted the potential application of E. purpurea subcritical water extracts as a source of valuable bioactive compounds.
Multi-objective casting production scheduling problem by a neighborhood structure enhanced discrete NSGA-II: an application from real-world workshop

Casting production scheduling problem has attracted increasing research attention in recent years to facilitate the profits, efficiency, and environment issues of casting industry. Casting is often characterized by the properties of intensive energy consumption and complex process routes, which motivate the in-depth investigation on construction of practical multi-objective scheduling models and development of effective algorithms. In this paper, for the first time, the multi-objective casting production scheduling problem (MOCPSP) is constructed to simultaneously minimize objectives of defective rate, makespan, and total energy consumption. Moreover, a neighborhood structure enhanced discrete NSGA-II (N-NSGA-II) is designed to better cope with the proposed MOCPSP. In the N-NSGA-II, the advantage of selection mechanism of NSGA-II is fully utilized for selecting non-dominate solution, three neighborhood structures are elaborately designed to strengthen the ability of the local search, and a novel solution generating approach is proposed to increase the diversity of solutions for global search. Finally, a real-world case is illustrated to evaluate the performance of the N-NSGA-II. Computational results show that the proposed N-NSGA-II obtains a wider range of non-dominated solutions with better quality compared to other well-known multi-objective algorithms.
Adverse Drug Reaction Detection Using Latent Semantic Analysis

: Detecting Adverse Drug Reactions (ADRs) is one of the important information for determining the view of the patient on one drug. Most studies have investigated the extraction of ADRs from social networks, in which users share their opinion on a particular medication. Some studies have used trigger terms to detect ADRs. Such studies showed remarkable performance in terms of extracting ADR. However, these terms only would not be sufficient since it needs to be extended periodically when new side effects or new medical-related entities are being discovered. In addition, the feature space with trigger terms would lack latent semantic. This study aims to propose a semantic method based on Latent Semantic Analysis (LSA) for improving the detection of ADR. A benchmark dataset has been used in the experiments along with several pre-processing operations that have been applied including stop word removal, tokenization and stemming with three classifiers that were trained on the proposed LSA, namely Support Vector Machine (SVM), Naïve Bayes (NB) and Linear Regression (LR). In addition, two representations of documents were used, namely Term Frequency (TF) and Term Frequency-Inverse Document Frequency (TF-IDF). Results showed that the proposed LSA outperformed the baseline extended trigger terms by achieving 82% of F-measure for the dataset. Such superiority highlights the use of LSA where the semantic correspondences could be identified correctly rather than using a predefined list of trigger terms.
Effect of Cold Storage on the Nutritive and Microbiological Quality of Fermented Soy Drink from Tamarind and Nono

The effect of cold storage on fermented soy drink from tamarind and nono was assessed. Soymilk was produced by milk extraction from whole soybean seeds and pasteurized at 76oC for 30 minutes. The soymilk was divided into two portions. One portion inoculated with tamarind pulp containing 5.3×103 cfu/mL and the other with nono containing 11.6×103 cfu/mL. They were incubated at 42oC for 12 hours, fermentation was harvested by stirring, packaged, refrigerated at 5oC and subjected to microbial analysis using standard method. Preservation of drink by refrigeration method increased the microbial load of sample A from day 0 (8.7×103 cfu/mL) to day 9 (15.0×103 cfu/mL) but decreased on day 12 (11×103 cfu/mL). Similar results were recorded for samples B and C. However, sample A had neither coliform nor fungal growth. Sample A and B had no significant (p>0.05) difference in energy value (41.91±0.89 and 42.50±1.14) but sample C had the highest energy (96.69±2.03- 77.80±1.17), ash (4.10±0.13- 96.69±2.03), crude protein (0.51±0.01- 0.55±0.03), oil extract (3.44±0.17- 3.65±0.15) and NFE (7.61±0.14- 11.16±0.17) but lowest in moisture (79.84±1.07- 80.27±1.30) contents on day 6– 12. However, sample B had high moisture content ranged (84.43±1.17- 87.15±2.3) but lower in other parameters. Statistical analysis for the vitamin C, potassium and calcium of sample’s A, B and C were carried to determine their significant differences. Refrigeration slows down the bacterial activity hence reducing spoilage thus making fermented soy drink a good source of desired protein in Nigeria.
User Satisfaction Survey on Public Transport by a New PAHP Based Model

There is an obvious trade-off between information obtained from passenger surveys and cost and time investment. This paper offers a new approach for this problem and its detailed step-by-step procedure description. Parsimonious Analytic Hierarchy Process (PAHP) is a recently created methodology that combines the simplicity of direct evaluations with the consistency and reliability of the Analytic Hierarchy Process (AHP). In the paper, the first large sample survey of passenger satisfaction by a new, PAHP-based model and procedure is presented as a case study. Moreover, a comparison with an AHP survey on the same public transport system and the same pattern are demonstrated. Since the comparative analysis produced a strong correlation between AHP and PAHP outcomes, it can be stated that the new procedure is less time consuming and costly than the AHP, while possessing the same benefits, and thus, it is more trustworthy than satisfaction measured by direct evaluations. Consequently, our proposed model can be applied both in theoretical and practical cases. Theoretically, it solves the problem of avoiding the use of large pairwise comparison matrices, and practically, it is a useful support to public satisfaction surveys, especially in the transportation sector.
Aktivitas Antioksidan Ekstrak Etanol Akar Kalakai (Stenochlaena palustris Bedd) Asal Kalimantan Tengah

ABSTRAK Tanaman khas Kalimantan yang banyak digunakan sebagai tanaman obat adalah kalakai atau sering juga disebut paku haruan (Stenochlaena palustris Bedd) atau pakis. Kalakai mengandung beberapa senyawa bioaktif seperti fenolik, flavonoid, alkaloid dan keluarga terpenoid yang telah terbukti sangat efektif sebagai antioksidan. Kalakai merupakan tumbuhan yang tumbuh subur di tanah gambut dan juga ditemukan tumbuh baik di tanah berpasir. Penelitian ini bertujuan mengetahui aktivitas antioksidan dari akar kalakai (Stenochlaena palustris) yang tumbuh pada tanah gambut dan tanah berpasir berdasarkan parameter Inhibitory Concentration 50 (IC50). Aktivitas antioksidan diuji menggunakan metode DPPH (2,2-difenil-1-pikril-hidrazil) dan kuersetin sebagai pembanding. Nilai IC50 untuk ekstrak akar kalakai pada tanah gambut yaitu sebesar 19,06 ppm dan pada ekstrak akar kalakai pada tanah pasir didapat IC50 sebesar 24,40 ppm. Hasil uji aktivitas antioksidan pada ekstrak etanol akar kalakai yang tumbuh pada tanah pasir dan tanah gambut memiliki aktivitas antioksidan yang sangat kuat. Kata Kunci : Akar, Antioksidan, Kalakai, Stenochlaena palustris Bedd ABSTRACT Borneo plants that are widely used as medicinal plants are kalakai or often also called paku Haruan (Stenochlaena palustris Bedd). Kalakai contains several bioactive compounds such as phenolics, flavonoids, alkaloids and terpenoid that have been shown to be very effective as antioxidants. Kalakai is a plant that thrives on peat soils and is also found to grow well in sandy soils. This study aims to determine the antioxidant activity of kalakai root (Stenochlaena palustris) that grows on peat soil and sandy soil based on Inhibitory Concentration 50 (IC50). Antioxidant activity was tested using DPPH method (2,2-diphenyl-1-picryl-hydrazil) and quercetin as a comparison. IC50 value for kalakai root extract on peat soil that is 19,06 ppm and at kalakai root extract on sand soil obtained IC50 that is 24,40 ppm. The results of antioxidant activity test on kalakai root extract that grow on sand and peat soil have very strong antioxidant activity. Keywords: Antioxidant, Kalakai, Root, Stenochlaena palustris Bedd
An Interactive Family Tourism Decision Model

To establish an interactive model reflecting how children’s involvement in family tourism decisions interacts with internal and external factors, this study examines the interrelationships between children’s individual characteristics (tourism knowledge and tourism attachment), parental characteristics (socio- and concept-orientation family communication patterns), and children’s influence on family tourism decision making (initiation and search/decision stages) using structural equation modeling. Results reveal that tourism knowledge and socio- and concept-orientation patterns are positively related to children’s influence on both decision stages. Specifically, tourism knowledge mediates relationships between socio- and concept-orientation patterns and children’s influence on the initiation stage; tourism attachment mediates the relationship between the concept-orientation pattern and children’s influence on the initiation stage. Lastly, theoretical and managerial implications of these findings are discussed.
Quantum -Chemical and Experimental Study of Synthesis a New Imine Ligand

A novel imine ligand TBTM [N,N'-(2,3,5,6-tetramethyl-1,4-phenylene)bis (1-(thiophen-2-yl)methanimine)] which has not been described in the literature Previously was synthesized by the condensation reaction of 2,3,5,6-tetramethylbenzene-1,4-diamine(TMBA)with thiophene-2-carbaldehyde (ThCA) then it was investigated using the density functional theory (DFT/B3LYP(6-311++ G(2d)) by finding the optimal structures for reacting and resulting compounds and also the calculation of cohesion and reaction energies in addition to the thermodynamic properties of these compounds.The calculations of the characterized thermodynamic functions for this reaction showed that the reaction is endothermic and It is found that the molar ratio of reaction is 1: 2. The analyzing of MO's energies, especially the energies of HOMO and LUMO of the reacting and resulting compounds showed that the formation reaction of this compound is not spontaneously. It is necessary to use a catalyst (as heat or chemical catalysis.etc.) for occurrence the reaction. Keywords: imine, DFT ,IR ,synthesis ,UV-visible ,structure.
A deep learning approach for noise reduction of off-axis computer generated holograms

In this research, we propose a deep-learning-based computer generated hologram generation algorithm. The algorithm is able to generate a de-noised off-axis computer generated hologram. A de-noising convolutional neural network (DnCnn) is trained with added non-Gaussian and non-stationary speckle noise. Signal noise ratio (SNR), peak signal to noise ratio (PSNR), and mean square error (MSE) are used to evaluate the performance of the DnCnn. What’s more, compared with the reconstructed image, the pixel distribution of the denoising image is closer to the original image. Results show that the algorithm is superior to conventional algorithms improvement about the quality of the reconstructed images.
Strategies towards expansion of chemical space of natural product-based compounds to enable drug discovery

Natural products (NPs) are an excellent source of biologically active molecules that provide many biologically biased features that enable innovative designing of synthetic compounds. NPs are characterized by high content of sp3 -hybridized carbon atoms; oxygen; spiro, bridged, and linked systems; and stereogenic centers, with high structural diversity. To date, several approaches have been implemented for mapping and navigating into the chemical space of NPs to explore the different aspects of chemical space. The approaches providing novel opportunities to synthesize NP-inspired compound libraries involve NP-based fragments and ring distortion strategies. These methodologies allow access to areas of chemical space that are less explored, and consequently help to overcome the limitations in the use of NPs in drug discovery, such as lack of accessibility and synthetic intractability. In this review, we describe how NPs have recently been used as a platform for the development of diverse compounds with high structural and stereochemical complexity. In addition, we show developed strategies aiming to reengineer NPs toward the expansion of NP-based chemical space by fragment-based approaches and chemical degradation to yield novel compounds to enable drug discovery.