Miracle-dz/UniFAR-FTUs huggingface dataset:
Antioxidant potential of bulk and nanoparticles of naringenin against cadmium-induced oxidative stress in Nile tilapia, Oreochromis niloticus

Abstract The study assessed the attenuating effect of citrus flavonone, naringenin, and its nanoformulation against cadmium (Cd) toxicity in Nile tilapia (Oreochromis niloticus). Nanoparticles of naringenin (NNn) were synthesized; size 165.1 nm (PDI 0.396) in variable shapes; cluster widespread, spherical accumulated, and tubular bacillary. Parallel, mature male Nile tilapia (n = 120 fish) were used for the exposure study. Group I was negative control. The groups II, III, and IV were exposed to 5 ppm of cadmium chloride monohydrate (CdCl2·H2O) for 21 days. Group III was treated with bulk naringenin (BNn) (3 ppm) and group IV was treated with NNn (3 ppm). Group V was exposed only to NNn (3 ppm). Cd-induced oxidative stress was evident from a significant increase in the hepatic malondialdehyde (MDA) levels and modulation of antioxidant enzymes in the liver and kidney. A significant increase in the hepatic metallothionein and HSP70 levels in the gills was observed. Treatment with both BNn and NNn significantly (p ≤ 0.05) reversed the Cd-induced alterations. However, the protective effect of nano naringenin was more profound on the bioaccumulation of Cd in liver and levels of HSP70 in the gills. These key findings could add to the commercial exploitation of naringenin and its nanosized form as a dietary therapeutic molecule. Graphical abstract Effect of naringenin and its nanoparticles on Cd-induced toxicity in Nile tilapia.
Deep convolutional neural networks for automatic coil pitches detection systems in induction motors

Abstract Stator winding structures are one of the most important parameters affecting motor performance in induction motor (IM). When deciding on the coil pitch, the winding structure and the power performance of the motor are also taken into consideration. The stator coil pitch of the IM is known at the design stage of the motor. The stator coil pitch of an IM manufactured and in use may be wanted to be changed with the desire to improve the performance of the motor and suppress some harmonics. In this case, it is necessary to determine the motor winding structure and coil pitch by opening the stator cover of the motor, removing the rotor, and manually examining the stator winding structure visually. However, this process prolongs this improvement process considerably. A system that can detect the stator coil pitch according to the stator current behavior while the motor is running can significantly shorten this improvement process. For this purpose, in this study, a deep convolutional neural network (DCNN) model that can automatically estimate IM stator coil pitch angle with an accuracy rate of 100% is designed and applied.
A meta-analysis of exosome in the treatment of spinal cord injury

Abstract Context There are no recommended therapeutic agents for acute spinal cord injury (SCI) due to the pathophysiological complexity of the injury. Objective The objective of this study is to investigate the efficacy of various exosomes and potential factors impacting the efficacy of exosomes. Methods We searched the PubMed, EMBASE, Web of Science, Medline, Scopus, and Cochrane Library databases to systematically collect articles comparing the locomotor function of SCI rodents undergoing exosome treatment and untreated SCI rodents. No language was preferred. Results Pooled analysis revealed that the locomotor function recovery of SCI rodents receiving exosomes was greatly improved (583 rats, 3.12, 95% CI: 2.56–3.67, p < 0.01; 116 mice, 2.46, 95% CI: 1.20–3.72, p < 0.01) compared to those of control rodents. The trial sequential analysis demonstrated the findings of the meta-analysis with the cumulative Z-curve crossing the upper monitoring boundary for the benefit and reaching the adjusted required information size. However, the origin of the exosome, SCI model, and administration method determined the therapeutic effect to some extent. Conclusions Despite the proven therapeutic effects of exosomes on SCI rodents, the results should be interpreted cautiously considering the diversity in vivo and in vitro in relation to future trials.
Three perspectives on parental support in youth soccer: Children, parents, and coaches

The aim of the research project was to gain an in-depth understanding of how children, coaches, and parents in youth soccer perceive parental support. In a preliminary study, a questionnaire was developed to assess satisfaction with parental support and sideline behavior in soccer. The multisection questionnaire was adapted to all three parties of interest—children, parents, and coaches—and administered to 260 parents, 196 coaches, and 111 male players aged 12–14 years across different clubs to identify factors influencing satisfaction with parental behavior across all three groups. Factor analysis led to a revised version of the questionnaire, emphasizing the importance of emotional support. The revised questionnaire was implemented in two consecutive field studies. Samples were drawn from an amateur youth soccer setting (Study 2, N = 316, players aged 8–13 years) and a professional youth academy (Study 3, N = 209, players aged 9–16 years). Overall, results indicate children and parents were highly satisfied with parental support and sideline behavior but coaches’ satisfaction differed significantly. The discussion therefore includes all three perspectives and provides recommendations on how to improve the coach–parent relationship and professional engagement with parents.
Properties of Knee Joint Position Sense Tests for Anterior Cruciate Ligament Injury: A Systematic Review and Meta-analysis

Background: Knee proprioception is believed to be deficient after anterior cruciate ligament (ACL) injury. Tests of joint position sense (JPS) are commonly used to assess knee proprioception, but their psychometric properties (PMPs) are largely unknown. Purpose: To evaluate the PMPs (reliability, validity, and responsiveness) of existing knee JPS tests targeting individuals with ACL injury. Study Design: Systematic review; Level of evidence, 4. Methods: PubMed, Allied and Complementary Medicine, CINAHL, SPORTDiscus, Web of Science, Scopus, CENTRAL, and ProQuest databases were searched to identify studies that assessed PMPs of knee JPS tests in individuals with ACL injury. The risk of bias for each included study was assessed and rated at the outcome level for each knee JPS test. Overall quality and levels of evidence for each PMP were rated according to established criteria. Meta-analyses with mean differences were conducted using random effects models when adequate data were available. Results: Included were 80 studies covering 119 versions of knee JPS tests. Meta-analyses indicated sufficient quality for known-groups and discriminative validity (ACL-injured knees vs knees of asymptomatic controls and contralateral noninjured knees, respectively), owing to significantly greater absolute errors for ACL-injured knees based on a strong level of evidence. A meta-analysis showed insufficient quality for responsiveness, which was attributed to a lack of significant change over time after diverse interventions with a moderate level of evidence. Statistical heterogeneity (I 2 > 40%) was evident in the majority of meta-analyses. All remaining PMPs (reliability, measurement error, criterion validity, convergent validity, and other PMPs related to responsiveness) were assessed qualitatively, and they failed to achieve a sufficient quality rating. This was a result of either the study outcomes not agreeing with the statistical cutoff values/hypotheses or the level of evidence being rated as conflicting/unknown or based on only a single study. Conclusion: Knee JPS tests appear to have sufficient validity in differentiating ACL-injured knees from asymptomatic knees. Further evidence of high methodologic quality is required to ascertain the reliability, responsiveness, and other types of validity assessed here. We recommend investigations that compare the modifiable methodologic components of knee JPS tests on their PMPs to develop standardized evidence-based tests.
In situ imaging of bacterial membrane projections and associated protein complexes using electron cryo-tomography

The ability to produce membrane projections in the form of tubular membrane extensions (MEs) and membrane vesicles (MVs) is a widespread phenomenon among bacteria. Despite this, our knowledge of the ultrastructure of these extensions and their associated protein complexes remains limited. Here, we surveyed the ultrastructure and formation of MEs and MVs, and their associated protein complexes, in tens of thousands of electron cryo-tomograms of ∼ 90 bacterial species that we have collected for various projects over the past 15 years (Jensen lab database), in addition to data generated in the Briegel lab. We identified MEs and MVs in 13 species and classified several major ultrastructures: 1) tubes with a uniform diameter (with or without an internal scaffold), 2) tubes with irregular diameter, 3) tubes with a vesicular dilation at their tip, 4) pearling tubes, 5) connected chains of vesicles (with or without neck-like connectors), 6) budding vesicles and nanopods. We also identified several protein complexes associated with these MEs and MVs which were distributed either randomly or exclusively at the tip. These complexes include a secretin-like structure and a novel crown-shaped structure observed primarily in vesicles from lysed cells. In total, this work helps to characterize the diversity of bacterial membrane projections and lays the groundwork for future research in this field.
2′,3′-cAMP treatment mimics abiotic stress response

For many years, 2′,3′-cyclic adenosine monophosphate (2′,3′-cAMP), a positional isomer of the second messenger 3′,5′-cAMP, has not received enough attention. Recent studies have reported that 2′,3′-cAMP exists in plants and might be involved in stress signaling because 1) its level increases upon wounding and 2) it was shown to participate in stress granule formation. Although 2′,3′-cAMP is known as RNA-degradation product, the effect of its accumulation in the cell remains unknown. Here, we unprecedentedly evaluate responses at the transcriptome, metabolome, and proteome levels to the accumulation of 2′,3′-cAMP in Arabidopsis plants. Data revealed that 2′,3′-cAMP is metabolized into adenosine, suggesting that a well-known cyclic nucleotide - adenosine pathway from human cells might exist also in plants. However, the control transcriptomic data indicated that, despite fewer overlaps, responses to 2′,3′-cAMP and to adenosine differ. Analysis of the transcriptome and proteome in response to 2′,3′-cAMP showed similar changes, known as signatures for abiotic stress response. This is further supported by the fact that adding to the role in facilitating of stress granules (SGs) formation, 2′,3′-cAMP induces changes at the level of proteins known as key components of SGs and can also induce movement of processing bodies (PBs) in the cell.
A complete reference genome improves analysis of human genetic variation

Compared to its predecessors, the Telomere-to-Telomere CHM13 genome adds nearly 200 Mbp of sequence, corrects thousands of structural errors, and unlocks the most complex regions of the human genome to clinical and functional study. Here we demonstrate how the new reference universally improves read mapping and variant calling for 3,202 and 17 globally diverse samples sequenced with short and long reads, respectively. We identify hundreds of thousands of novel variants per sample—a new frontier for evolutionary and biomedical discovery. Simultaneously, the new reference eliminates tens of thousands of spurious variants per sample, including up to 12-fold reduction of false positives in 269 medically relevant genes. The vast improvement in variant discovery coupled with population and functional genomic resources position T2T-CHM13 to replace GRCh38 as the prevailing reference for human genetics. One Sentence Summary The T2T-CHM13 reference genome universally improves the analysis of human genetic variation.
An enhanced inverted encoding model for neural reconstructions

Here we present a more interpretable and versatile approach for reconstructing the contents of perception, attention, and memory from neuroimaging data. Our enhanced inverted encoding model (eIEM) incorporates theoretical and methodological improvements including proper accounting of population-level tuning functions and a trial-by-trial prediction error-based metric where reconstruction quality is measured in meaningful units. Added functionality and improved flexibility is further gained via eIEM’s novel goodness-of-fit feature: for trial-by-trial reconstructions, goodness-of-fits are obtained independently (non-circularly) to prediction error and can be applied to any IEM procedure or decoding metric, resulting in improved reconstruction quality and brain-behavior correlations, and more creative applications. We validate eIEM from methodological principles, simulated neuroimaging datasets, and three pre-existing fMRI datasets spanning perception, attention, and working memory. Notably, eIEM is easy to apply and broadly accessible – our Python package (https://pypi.org/project/inverted-encoding) implements eIEM in one line of code – and is easily modifiable to compare performance metrics and/or scale up to more complex models.
A Multi-agent Reinforcement Learning Method for Role Differentiation Using State Space Filters with Fluctuation Parameters

Recently, there have been many studies on Multi-agent Reinforcement Learning (MARL) in which each autonomous agent obtains its own control rule by RL. Here, we hypothesize that different agents having individuality is more effective than uniform agents in terms of role differentiation in MARL. We have previously proposed a promoting method of role differentiation using a waveform changing parameter in
Aggregation for Privately Trained Different Types of Local Models

Machine learning has been a thriving topic in recent years, with many practical applications and active research aspects. In machine learning, model aggregation is an important area. The idea of model aggregation is to aggregate a global model from trained local models. However, traditional aggregation methods based on parameter averaging can not aggregate models which have different types and structures. Because parameter averaging will fail to average different types of values (parameters). To address this problem, we propose a new aggregation method which will suit for different types of local models. To achieve our goal, we transfer knowledge from local models to the global model. To do so, firstly, we propose differentially private GANs, let local parties generate synthetic data related to their training data. Secondly, we use the majority of prediction votes from local models to label those synthetic samples. Finally, use the labelled synthetic data to train the global model. By combining synthetic data and labels from local models, knowledge can be transferred from local models to the global model. We evaluate our scheme on Adult, MNIST and Fashion MNIST datasets under different settings, experimental results show that our scheme can achieve an accurate global model with low privacy loss. Besides, the easily implemented building blocks make our scheme efficient and practical for applications.
Leader-Following Regional Multiple-Bipartite Consensus for Networked Lagrangian Systems with Coopetition Interactions

This paper investigates the leader-following regional multiple-bipartite consensus problems of networked Lagrangian systems (NLSs) in coopetition networks. Our framework expands the application scopes of traditional regional consensus in cooperative networks. With the aid of a novel auxiliary variable embedded in the control protocols, the final states of NLSs are guaranteed to realise multi-regional symmetry in the constructed multiple symmetric regions. By utilising the characteristic of acyclic topology in the structurally balanced graph, the stability of the closed system is performed by perturbation analysis theory, nonlinear control theory, functional analysis theory, and so on. Finally, the effectiveness of our approach is verified by numerical simulations.
deMSF: a Method for Detecting Malicious Server Flocks for Same Campaign

Nowadays, cybercriminals tend to leverage dynamic malicious infrastructures with multiple servers to conduct attacks, such as malware distribution and control. Compared with a single server, employing multiple servers allows crimes to be more efficient and stealthy. As the necessary role infrastructures play, many approaches have been proposed to detect malicious servers. However, many existing methods typically target only on the individual server and therefore fail to reveal inter-server connections of an attack campaign. In this paper, we propose a complementary system, deMSF, to identify server flocks, which are formed by infrastructures involved in the same malicious campaign. Our solution first acquires server flocks by mining relations of servers from both spatial and temporal dimensions. Further we extract the semantic vectors of servers based on word2vec and build a textCNN-based flocks classifier to recognize malicious flocks. We evaluate deMSF with real-world traffic collected from an ISP network. The result shows that it has a high precision of 99% with 90% recall.
Using Agent-Based Models for Prediction in Complex and Wicked Systems

: This paper uses two thought experiments to argue that the complexity of the systems to which agent-based models (ABMs) are often applied is not the central source of difficulties ABMs have with prediction. We define various levels of predictability, and argue that insofar as path-dependency is a necessary attribute of a complex system, ruling out states of the system means that there is at least the potential to say something useful. ‘Wickedness’ is argued to be a more significant challenge to prediction than complexity. Critically, however, neither complexity nor wickedness makes prediction theoretically impossible in the sense of being formally undecidable computationally-speaking: intractable being the more apt term given the exponential sizes of the spaces being searched. However, endogenous ontological novelty in wicked systems is shown to render prediction futile beyond the immediately short term.
Independent Category Classifiers for Emergency Scene Description using Deep Learning approaches

In this work, we present our proposed model for Disaster Scene Description and Indexing (DSDI) Challenge of TRECVIDI2020. For the challenge we used: the LADI Dataset, a dataset composed of scrapped Google Images using as a keyword the name of the label and an extended set of the LADI dataset. This extension was created using a crowdsourcing service like Amazon Mechanical Turk. As approaches we tried diﬀerent combination of Convolutional Neural Networks (CNN), what worked better for us was using ﬁve diﬀerent classiﬁers, one for each category of the LADI dataset. We used this conﬁguration because we noticed that dividing the task lead to better scores. Indeed by checking the results it is possible to notice that dividing the task help the model to learn speciﬁc features for that category. We found that the dataset is very challenging and it is diﬃcult for a model trained end to end to learn all the features useful to detect a class. For this reason, an ensemble model approach worked better for the challenge. We think that more sophisticated label for example segmentation map could have allowed obtaining better results.
A Low-Complexity Three-Stage Estimator for Low-Rank mmWave Channels

This paper investigates the estimation of low-rank multiple-input multiple-output (MIMO) channels in millimeter wave (mmWave) communications. Hybrid MIMO transceivers equipped with uniform linear arrays (ULAs) and phase shifter networks are considered. We propose a novel three-stage channel estimator by exploiting the low-rankness of the channel matrix and knowledge of the array response: We first obtain a low-rank estimate of the channel matrix using inductive matrix completion (IMC); then estimate the angle of arrival (AoA) and angle of departure (AoD) for the propagation paths by solving two one-dimensional spectrum estimation problems using Toeplitz rectification and the root multiple signal classification (MUSIC) algorithm; and finally pair the AoAs and AoDs and estimate the channel gains by solving a sparse recovery problem. Each stage is implemented at a low complexity and thus the overall complexity is kept low. A priori knowledge about the channel is exploited progressively to enhance the performance. The simulation results suggest significant gains in the channel estimation performance along with sparse representations of the estimated channel.
Application of Inertial and GNSS Integrated Navigation to Seabird Biologging

The study demonstrates the versatility of integration of inertial navigation and global navigation satellite system (GNSS) with its unique application to seabird biologging. Integrated navigation was originally developed in the field of aerospace engineering, which requires accurate and reliable position, velocity, and attitude information for the guidance and control of aircraft and spacecraft. Due to its high performance and recent progress of sensor development, integrated navigation has been widely used not only in aerospace but also in many fields represented by land and marine vehicles. One of its ultimate applications under the constraint on the size and power consumption of devices is this study. Seabird biologging involves attaching a logging device onto a seabird for scientific purposes to understand its biomechanics, behavior, and so on. Design restrictions for the device include several tens of grams mass, several tens of millimeters in length, and several tens of milliamperes of power consumption. It is more difficult to maintain the accuracy of such a device than applications to an artificial vehicle. This study has shown that integrated navigation is a feasible solution for such extreme applications with two examples: biologging for wandering albatrosses and great frigatebirds. Furthermore, it should be stressed that the navigation captured the world’s first data of their detailed trajectories and attitudes in their dynamic and thermal soarings. For completeness, the navigation algorithm, simulation results to show the effectiveness of the algorithm, and the logging devices attached to bird are also described.
Studies on Determination of Metronidazole and Tinidazole in Breast - feeding Women

Objective: To study analysis of metronidazole and tinidazole in breast - feeding women. Method: HPLC was applied to quantitative analysis. Ultrasphere - ODS column was used. For metronidazole, the mobile phase was composed of acetonitrile - 0. 1% HAc (50: 50). For tinidazole, the mobile phase was composed of acetonitrile - water (60:40). Flow rate was 1.0 mL · min-1 . The UV detection wavelength was 318 nm. Re-sults: The mean metronidazole concentration in breast milk was (10. 11 ±5. 390) μg ·mL-1 in 2 h after intrave-nous infusion and (4.478±2.669) μg · mL-1 8 h later. The mean tinidazole concentration in breast milk was (14. 17±5. 746) μg · mL-1 in 2 h after intravenous infusion and (9. 364 ±5. 123) μg · mL-1 8 h later. Con-clusion: The method is simple, sensitive and suitable for the study. Based upon the result of study, breast -feeding women was proposed the rational way.