Miracle-dz/UniFAR-FTUs huggingface dataset:
Literature Mining of Disease Associated Noncoding RNA in the Omics Era

Noncoding RNAs (ncRNA) are transcripts without protein-coding potential that play fundamental regulatory roles in diverse cellular processes and diseases. The application of deep sequencing experiments in ncRNA research have generated massive omics datasets, which require rapid examination, interpretation and validation based on exiting knowledge resources. Thus, text-mining methods have been increasingly adapted for automatic extraction of relations between an ncRNA and its target or a disease condition from biomedical literature. These bioinformatics tools can also assist in more complex research, such as database curation of candidate ncRNAs and hypothesis generation with respect to pathophysiological mechanisms. In this concise review, we first introduced basic concepts and workflow of literature mining systems. Then, we compared available bioinformatics tools tailored for ncRNA studies, including the tasks, applicability, and limitations. Their powerful utilities and flexibility are demonstrated by examples in a variety of diseases, such as Alzheimer’s disease, atherosclerosis and cancers. Finally, we outlined several challenges from the viewpoints of both system developers and end users. We concluded that the application of text-mining techniques will booster disease-associated ncRNA discoveries in the biomedical literature and enable integrative biology in the current omics era.
Optimization of Crude Palm (Elaeis guineensis) Oil Bleaching using Zeolite-Fe by Response Surface Methodology

A carcinogenic 3-monochloropropane 1, 2 diol ester (3-MCPD ester) can be formed during processing crude palm oil (CPO). Chlorine is one of the precursors for the formation of 3-MCPD esters. This study aimed to optimize the bleaching conditions using zeolite-Fe for reducing chlorine concentration by the Response Surface Methodology (RSM) and further evaluate the characteristics of the bleached CPO. Factors such as bleaching time, zeolite-Fe concentration, and bleaching temperature were evaluated and further optimized. The results showed that Fe in the modified zeolite-Fe increased about 71.89% compared to natural zeolite. Zeolite-Fe concentration, bleaching time, and bleaching temperature had a significant effect on chlorine adsorption. The optimum bleaching process was obtained at a zeolite-Fe concentration of 5% (w/w) and bleaching temperature of 80 °C for 30 minutes. Bleached CPO had a chlorine concentration of 25 ± 1 ppb, carotenoid of 467.70 ± 13.71 ppm, and a DOBI (Deterioration Of Bleachability Index) value of 2.17 ± 0.01 R.
Tropical precipitation patterns in response to a local warm SST area placed at the equator of an aqua planet

Aqua planet experiments are performed in order to investigate the effects of an equatorial warm sea surface temperature (SST) area on the tropical large-scale precipitation patterns. The numerical model utilized is a spherical three-dimensional primitive system with resolution of T42L16 and with simplified hydrological processes. The warm SST area is placed at the equator of an aqua planet whose basic SST distribution is zonally uniform and symmetric about the equator. The calculated tropical precipitation distributions are characterized by the appearance of an east-west asymmetry; precipitation decreases to the west of the warm SST area, while it increases in the longitudinally wide area to the east. The east-west asymmetry appears regardless of the cumulus parameterization schemes utilized (the convective adjustment scheme and the Kuo scheme). In the western region to the warm SST area, an increase of the stability due to a temperature rise in the middle layer is observed and correspondingly downward flow is recognized. These are consistent with the decrease of precipitation. In the eastern region, although a temperature rise appears in the middle layer, the stability decreases due to the increase of water vapor in the lower layer caused by the meridional moisture convergence. This destabilization is consistent with the increase of precipitation. Experiments in which the wind velocity used in the evaluation of the surface evaporation is fixed are performed to show that WISHE (Wind Induced Surface Heat Exchange) mechanism is not a principal cause for the generation of the east-west asymmetry of the precipitation pattern.
Synthesis, In Silico, and In Vitro Evaluation of Anti-Leishmanial Activity of Oxadiazoles and Indolizine Containing Compounds Flagged against Anti-Targets

Due to the lack of approved vaccines against human leishmaniasis and the limitations of the current chemotherapy inducing side effects and drug resistance, development of new, effective chemotherapeutic agents is essential. This study describes the synthesis of a series of novel oxadiazoles and indolizine-containing compounds. The compounds were screened in silico using an EIIP/AQVN filter followed by ligand-based virtual screening and molecular docking to parasite arginase. Top hits were further screened versus human arginase and finally against an anti-target battery to tag their possible interactions with proteins essential for the metabolism and clearance of many substances. Eight candidate compounds were selected for further experimental testing. The results show measurable in vitro anti-leishmanial activity for three compounds. One compound with an IC50 value of 2.18 µM on Leishmania donovani intramacrophage amastigotes is clearly better positioned than the others as an interesting molecular template for further development of new anti-leishmanial agents.
Metabolic Profiling of Inga Species with Antitumor Activity

This work evaluated the metabolic profiling of Inga species with antitumor potential. In addition, we described the antigenotoxicity of polyphenols isolated from I. laurina and a proteomic approach using HepG2 cells after treatment with these metabolites. The in vitro cytotoxic activity against HepG2, HT-29 and T98G cancer cell lines was investigated. The assessment of genotoxic damage was carried out through the comet assay. The ethanolic extract from I. laurina seeds was subjected to bioassay-guided fractionation and the most active fractions were characterized. One bioactive fraction with high cytotoxicity against HT-29 human colon cancer cells (IC50 = 4.0 µg mL−1) was found, and it was characterized as a mixture of p-hydroxybenzoic acid and 4-vinyl-phenol. The I. edulis fruit peel (IC50 = 18.6 µg mL−1) and I. laurina seed (IC50 = 15.2 µg mL−1) extracts had cytotoxic activity against the cell line T98G, and its chemical composition showed a variety of phenolic acids. The chemical composition of this species indicated a wide variety of aromatic acids, flavonoids, tannins, and carotenoids. The high concentration (ranging from 5% to 30%) of these polyphenols in the bioactive extract may be responsible for the antitumor potential. Regarding the proteomic approach, we detected proteins directly related to the elimination of ROS, DNA repair, expression of tumor proteins, and apoptosis.
Deep Deterministic Policy Gradient to Minimize the Age of Information in Cellular V2X Communications

This paper studies the problem of minimizing the age of information (AoI) in cellular vehicle-to-everything communications. To provide minimal AoI and high reliability for vehicles’ safety information, non-orthogonal multiple access is exploited. We reformulate a resource allocation problem that involves half-duplex transceiver selection, broadcast coverage optimization, power allocation, and resource block (RB) scheduling. First, to obtain the optimal solution, we formulate the problem as a mixed-integer nonlinear programming problem and then study its NP-hardness. The negative result of NP-hardness motivates us to design efficient sub-optimal solutions. Consequently, we model the problem as a single-agent Markov decision process (MDP). The MDP model helps in solving the problem efficiently using fingerprint deep reinforcement learning (DRL) techniques such as deep-Q-network (DQN) methods. Nevertheless, applying DQN is not straightforward due to the curse of dimensionality implied by the large and mixed action space that contains discrete RB scheduling decisions and continuous power and coverage optimization decisions. Therefore, to solve this mixed discrete/continuous problem efficiently simply and elegantly, we propose a decomposition technique that consists of first solving the discrete subproblem using a matching algorithm based on state-of-the-art stable roommate matching and then solving the continuous subproblem using DRL algorithm that is based on deep deterministic policy gradient (DDPG). We validate our proposed method through Monte Carlo simulations where we show that the decomposed matching and DRL algorithm successfully minimizes the AoI and achieves almost 66% performance gain compared to the best benchmarks for various vehicles’ speeds, transmission power, or packet sizes. Further, we prove the existence of an optimal value of broadcast coverage at which the learning algorithm provides the optimal AoI.
Overexpressing Phytochrome Interacting Factor 8 of Myrothamnus flabellifolia Enhanced Drought and Salt Tolerance in Arabidopsis

Myrothamnus flabellifolia is the only woody resurrection plant found in the world and can survive from long-term desiccation. Therefore, M. flabellifolia could be considered as a valuable resource for study of plant adaptation to abiotic stress. However, few genes related to its drought tolerance have been functionally characterized and the molecular mechanisms underlying the stress tolerance of M. flabellifolia are largely unknown. The phytochrome interacting factor (PIF) family is a group of basic helix–loop–helix (bHLH) transcription factors and functions as the core regulator in plant growth and development. However, less is known of its participation in abiotic stress response. In this study, we isolated and characterized a dehydration-inducible PIF gene MfPIF8 from M. flabellifolia. Heterologous expression of MfPIF8 in Arabidopsis enhanced tolerance to drought and salinity stresses at seedling and adult stages. It significantly increased primary root length and stomatal aperture (ration of length/width) under stress treatments and decreased water loss rate. Compared with WT, the transgenic lines overexpressing MfPIF8 exhibited higher chlorophyll content and lower malondialdehyde accumulation. The abilities of osmotic adjustment and reactive oxygen species scavenging were also enhanced in MfPIF8 transgenic lines. These results suggest that MfPIF8 may participate in the positive regulation of abiotic stress responses. Additional investigation of its mechanism is needed in the future.
Single-Sample Face Recognition Based on Feature Expansion

Face recognition (FR) with a single sample per person (SSPP) is one of the most challenging problems in computer vision. In this scenario, it is difficult to predict facial variation such as pose, illumination, and disguise due to the lack of enough training samples. Therefore, the development of the FR system with only a small number of training samples is hindered. To address this problem, this paper proposes a scheme combined transfer learning and sample expansion in feature space. First, it uses transfer learning by training a deep convolutional neural network on a common multi-sample face dataset and then applies the well-trained model to a target data set. Second, it proposes a sample expansion method in feature space called k class feature transfer (KCFT) to enrich intra-class variation information for a single-sample face feature. Compared with other expanding sample methods in the image domain, this method of expanding the samples in the feature domain is novel and easy to implement. Third, it trains a softmax classifier with expanded face features. The experimental results on ORL, FERET, and LFW face databases demonstrate the effectiveness and robustness of the proposed method for various facial variations.
Blockchain Applications – Usage in Different Domains

Originally conceived as a mechanism to enable a trustless cryptocurrency—Bitcoin, blockchain has since unbound itself from its original purpose as an increasing number of industries and stakeholders’ eye the technology as an attractive alternative to solve existing business solutions as well as disrupt mature industries. This paper presents a systematic literature review of the blockchain technology, tracking its increase in popularity in relation to similar technologies, such as cryptocurrencies and Bitcoin. The objective of this paper is to identify the current standing of the blockchain technology within the literature while also identifying the major fields of study and areas of application for which blockchain offers a valuable solution. This paper finds that unique features to the blockchain, such as privacy, security, anonymity, decentralization, and immutability, provide valuable benefits to various fields and subjects. This paper also finds that exploring the application of blockchain has only begun with some limited studies in areas, such as the Internet of Things, energy, finance, healthcare, and government, that also stand to benefit disproportionately from its implementation.
Baseline Characteristics Associated with Good Collateral Status Using Hypoperfusion Index as an Outcome

Up to 30% of ischemic stroke cases are due to large vessel occlusion (LVO), causing significant morbidity. Studies have shown that the collateral circulation of patients with acute ischemic stroke (AIS) secondary to LVO can predict their clinical and radiological outcomes. The aim of this study is to identify baseline patient characteristics that can help predict the collateral status of these patients for improved triage. In this IRB approved retrospective study, consecutive patients presenting with AIS secondary to anterior circulation LVO were identified between September 2019 and August 2021. The baseline patient characteristics, laboratory values, imaging features and outcomes were collected using a manual chart review. From the 181 consecutive patients initially reviewed, 54 were confirmed with a clinical diagnosis of AIS and anterior circulation LVO. In patients with poor collateral status, the body mass index (BMI) was found to be significantly lower compared to those with good collateral status (26.4 ± 5.6 vs. 31.7 ± 12.3; p = 0.045). BMI of >35 kg/m2 was found to predict the presence of good collateral status. Age was found to be significantly higher (70.5 ± 9.6 vs. 58.9 ± 15.6; p = 0.034) in patients with poor collateral status and M1 strokes associated with older age and BMI.
Reinforcement Learning for Robot Navigation in Nondeterministic Environments

Mobile robots are commonly used for missions like target searching and security surveillance in unknown environments, where an exact mathematical model may not be available. In this paper, we formulate the problem of mobile robot path planning in unknown environments as a nondeterministic Markov Decision Process (MDP), and provide a model-free reinforcement learning solution in which the modified Q-learning utilizes a combined ε-greedy and Boltzmann exploration. We simulate the validity of the proposed algorithm, and compare the learning process with that of the original Q-learning algorithm. We also analyze the effects of the discounted factor on learning results. Simulations show that the proposed algorithm can generate the shortest path that obtains the maximized accumulated reward in environments having nondeterministic Markovian property given appropriate values of the discounted factor.
Modification of the DIBR and MABAC Methods by Applying Rough Numbers and Its Application in Making Decisions

This study considers the problem of selecting an anti-tank missile system (ATMS). The mentioned problem is solved by applying a hybrid multi-criteria decision-making model (MCDM) based on two methods: the DIBR (Defining Interrelationships Between Ranked criteria) and the MABAC (Multi-Attributive Border Approximation area Comparison) methods. The methods are modified by applying rough numbers, which present a very suitable area for considering uncertainty following decision-making processes. The DIBR method is a young method with a simple mathematical apparatus which is based on defining the relation between ranked criteria, that is, adjacent criteria, reducing the number of comparisons. This method defines weight coefficients of criteria, based on the opinion of experts. The MABAC method is used to select the best alternative from the set of the offered ones, based on the distance of the criteria function of every observed alternative from the border approximate area. The paper has two main innovations. With the presented decision-making support model, the ATMS selection problem is raised to a higher level, which is based on a proven mathematical apparatus. In terms of methodology, the main innovation is successful application of the rough DIBR method, which has not been treated in this way in the literature so far. Additionally, an analysis of the literature related to the research problem as well as to the methods used is carried out. After the application of the model, the sensitivity analysis of the output results of the presented model to the change of the weight coefficients of criteria is performed, as well as the comparison of the results of the presented model with other methods. Finally, the proposed model is concluded to be stable and multi-criteria decision-making methods can be a reliable tool to help decision makers in the selection process. The presented model has the potential of being applied in other case studies as it has proven to be a good means for considering uncertainty.
Human Digital Twin for Personalized Healthcare: Vision, Architecture and Future Directions

Human digital twin (HDT) is an emerging technology that can revolutionize the existing healthcare system with the ability to enable personalized healthcare services (PHS) using various tools such as artificial intelligence and blockchain. Its implementation is expected to be similar to digital twins proposed in other application areas such as manufacturing and aviation by consisting of three key dimensions: the physical entity, virtual model, and connection that characterizes the physical-virtual interactions. The known complexity of human body structure because of the constant molecular and physiological changes, however, means extracting precise medical data as well as modeling of HDT is very difficult. Hence, HDT is much more complex than its counterparts and its implementation methods remain unclear, which deserves further investigation. This article thus presents the architectural framework and key design requirements of HDT and then discusses the key technologies and technical challenges to suggest future directions. We believe that this article will open up new research opportunities and motivate new research efforts towards the development of HDT for PHS.
Psychomotor performance in video games

Introduction: Interactive electronic games allow to access virtual environments and interact using a computer or TV screen. Anyone who has played a video game, or seen others playing, is aware of the importance of reaction speed and eye-hand coordination skills. Objective: To determine the differences in psychomotor performance between professional gamers and amateurs. Material and Methods: A total of 62 gamers took part in the study, including 31 people - professional video game users who had participated in e-tournaments in the last month (age: M = 20.6, SD = 6.3) and 31 people who did not play video games or played very rarely (they declared that they did not participate in e-sports tournaments), who constituted the control group (age: M = 17.9, SD = 5.4). Integrated computer SDP-System with an executive module for stimulus generation and reception was used to assess psychomotor performance. Results: Professional gamers have higher psychomotor skills than amateurs. They reveal better results for fast thinking, motor reactions, perception, attention, and working memory. Conclusion: Playing video games has a positive impact on players' psychomotor performance and can promote improvements in elementary cognitive functions. Key words: video-games; e-sport; psychomotor performance; cognitive functions
Robust Timing Error Detection for Multilevel Baud-Rate CDR

Baud rate timing recovery, for which the Mueller-Muller approach is the most prominent, has become the mainstay of high speed serial links because of the simplicity of its implementation. The foundations of Mueller-Muller - Type A timing function and its propensity to lock to erroneous sampling points when applied to high order pulse amplitude modulation is studied. Using the timing error detector characteristics, the probability of the Mueller-Muller - Type A phase detector (PD) to settle to undesirable lock points is investigated. The impact of prevailing approaches to mitigate the effect of this behavior on the performance of clock and data recovery (CDR) systems is quantified, and a new low-overhead PD approach without any of their drawbacks is proposed. Simulink simulations of the CDR phase acquisition, jitter tolerance and timing margins are used to demonstrate the effectiveness of the proposed PD in achieving superior CDR performance while avoiding any of the convergence issues.
Effect of Worry Level on Recall Memory for Odors in ApoE-ε4 Carriers and Non-Carriers

Abstract Objective: Increased levels of worry, age, and presence of the apolipoprotein-E (ApoE)-ε4 allele are associated with the risk of developing cognitive declines and Alzheimer’s disease (AD). Odor memory performance has been shown to vary as a function of age and ApoE genotype, and odor memory tests are sensitive to preclinical AD. Worry is known to influence verbal memory; however, its effects on odor memory are unknown. This study aimed to assess the relationships between worry, age, and ε4 status on odor memory. Method: Worry was evaluated for young (n = 53) and older (n = 45) adults using the Penn State Worry Questionnaire. Odor memory was assessed using the California Odor Learning Test, an olfactory analogue to the California Verbal Learning Test. Results: A significant main effect of worry on long-delay free recall was found, such that increasing worry was associated with better recall across age and ε4 status. A significant interaction effect between ε4 status and worry on both short-and long-delay cued recall was found, such that across age, higher worry was associated with increased cued recall scores among ε4-negative adults, and decreased scores among ε4-positive adults. Conclusions: Findings demonstrated that worry influences odor memory and exerts a particular effect on cued recall among ε4 carriers who are at a greater risk of developing AD. Worry is a modifiable predictor of cognitive decline and risk of dementia in aging. Future studies on the effects of treatments aimed at reducing worry (e.g., cognitive behavioral therapies for anxiety) on changes in cognitive functioning are warranted.