Miracle-dz/UniFAR-FTUs huggingface dataset:
Characterization of Organellar-Specific ABA Responses during Environmental Stresses in Tobacco Cells and Arabidopsis Plants

Abscisic acid (ABA) is a critical phytohormone involved in multifaceted processes in plant metabolism and growth under both stressed and nonstressed conditions. Its accumulation in various tissues and cells has long been established as a biomarker for plant stress responses. To date, a comprehensive understanding of ABA distribution and dynamics at subcellular resolution in response to environmental cues is still lacking. Here, we modified the previously developed ABA sensor ABAleon2. 1_Tao3 (Tao3) and targeted it to different organelles including the endoplasmic reticulum (ER), chloroplast/plastid, and nucleus through the addition of corresponding signal peptides. Together with the cytosolic Tao3, we show distinct ABA distribution patterns in different tobacco cells with the chloroplast showing a lower level of ABA in both cell types. In a tobacco mesophyll cell, organellar ABA displayed specific alterations depending on osmotic stimulus, with ABA levels being generally enhanced under a lower and higher concentration of salt and mannitol treatment, respectively. In Arabidopsis roots, cells from both the meristem and elongation zone accumulated ABA considerably in the cytoplasm upon mannitol treatment, while the plastid and nuclear ABA was generally reduced dependent upon specific cell types. In Arabidopsis leaf tissue, subcellular ABA seemed to be less responsive when stressed, with notable increases of ER ABA in epidermal cells and a reduction of nuclear ABA in guard cells. Together, our results present a detailed characterization of stimulus-dependent cell type-specific organellar ABA responses in tobacco and Arabidopsis plants, supporting a highly coordinated regulatory network for mediating subcellular ABA homeostasis during plant adaptation processes.
Flexible iterative generalized demodulation filtering for the fault diagnosis of rotating machinery under nonstationary conditions

The health monitoring and diagnosis of rotating machinery under nonstationary conditions are still challenging due to the complex modulation characteristics and the interfering noises. In this paper, a novel flexible iterative generalized demodulation filtering method is proposed for the machinery fault diagnosis. First, the Hilbert transform is applied to the vibration signals to highlight the characteristic frequencies as well as their harmonics. Second, the phase functions used for mapping the interest frequencies are designed, and no matter how the speed varies, the time-varying frequencies of different signal segments with the same physical meaning are transformed into the same constant frequencies. Then, the filters are designed based on the introduced base frequency and the characteristic coefficients, and then the modulation rotating frequency, fault characteristic frequencies, and their harmonics are filtered. Finally, the demodulated signals are reconstructed and the health conditions are determined by the demodulated spectrums. The method is evaluated by the vibration signals of faulty rolling bearings and planetary gearboxes. The results demonstrate that the method can well reveal the fault-related frequencies and that the demodulated frequency values are not subject to the speed fluctuation profiles.
Overlapping Mechanisms of Action of Brain-Active Bacteria and Bacterial Metabolites in the Pathogenesis of Common Brain Diseases

The involvement of the gut microbiota and the metabolites of colon-residing bacteria in brain disease pathogenesis has been covered in a growing number of studies, but comparative literature is scarce. To fill this gap, we explored the contribution of the microbiota–gut–brain axis to the pathophysiology of seven brain-related diseases (attention deficit hyperactivity disorder, autism spectrum disorder, schizophrenia, Alzheimer’s disease, Parkinson’s disease, major depressive disorder, and bipolar disorder). In this article, we discussed changes in bacterial abundance and the metabolic implications of these changes on disease development and progression. Our central findings indicate that, mechanistically, all seven diseases are associated with a leaky gut, neuroinflammation, and over-activated microglial cells, to which gut-residing bacteria and their metabolites are important contributors. Patients show a pro-inflammatory shift in their colon microbiota, harbouring more Gram-negative bacteria containing immune-triggering lipopolysaccharides (LPS) in their cell walls. In addition, bacteria with pro-inflammatory properties (Alistipes, Eggerthella, Flavonifractor) are found in higher abundances, whereas lower abundances of anti-inflammatory bacteria (Bifidobacterium, Coprococcus, Eucbacterium, Eubacterium rectale, Faecalibacterium, Faecalibacterium prasunitzii, Lactobacillus, Prevotella, Roseburia) are reported, when compared to healthy controls. On the metabolite level, aberrant levels of short-chain fatty acids (SCFAs) are involved in disease pathogenesis and are mostly found in lower quantities. Moreover, bacterial metabolites such as neurotransmitters (acetylcholine, dopamine, noradrenaline, GABA, glutamate, serotonin) or amino acids (phenylalanine, tryptophan) also play an important role. In the future, defined aberrations in the abundance of bacteria strains and altered bacterial metabolite levels could likely be possible markers for disease diagnostics and follow-ups. Moreover, they could help to identify novel treatment options, underlining the necessity for a deeper understanding of the microbiota–gut–brain axis.
Analytic Feedback System Design: An Interpolation Approach

From the Publisher: This supplement presents the use of a technique derived from research conducted over the last 20 years. This technique, called interpolation theory in analytic design, is still not found in major introductory textbooks. Most introductory-level books teach trial and error design methods, not analytic design. Using this new technique, a mathematical existence theorem, and a solution algorithm, are posited and then used to solve a problem. Interpolation theory, which requires a minimal amount of mathematics, is the technique used to solve analytical design problems. The analytical design techniques presented in this book reflect modern approaches to problem-solving developed over the past 20 years (especially in the area of frequency domain), and are thoroughly up-to-date, as compared to the trial-and-error approach, which is now more than 30 years old.
Organoleptic Chemical Markers of Serpa PDO Cheese Specificity

Serpa is a protected designation of origin cheese produced with a vegetable coagulant (Cynara cardunculus L.) and raw ovine milk. Despite the unique sensory profile of raw milk cheeses, numerous parameters influence their sensory properties and safety. To protect the Serpa cheese quality and contribute to unifying their distinctive features, some rheologic and physicochemical parameters of cheeses from four PDO producers, in distinct seasons and with different sensory scores, were monitored. The results suggested a high chemical diversity and variation according to the dairy, month and season, which corroborates the significant heterogeneity. However, a higher incidence of some compounds was found: a group of free amino acids (Glu, Ala, Leu, Val and Phe), lactic and acetic acids, some volatile fatty acids (e.g., iC4, iC5, C6 and C12) and esters (e.g., ethyl butanoate, decanoate and dodecanoate). Through the successive statistical analysis, 13 variables were selected as chemical markers of Serpa cheese specificity: C3, C4, iC5, C12, Tyr, Trp, Ile, 2-undecanone, ethyl isovalerate, moisture content on a fat-free basis, the nitrogen-fractions (maturation index and non-protein and total nitrogen ratio) and G’ 1 Hz. These sensory markers’ identification will be essential to guide the selection and development of an autochthonous starter culture to improve cheese quality and safety issues and maintain some of the Serpa authenticity.
Mitochondrial genome of the critically Endangered silver boa (Chilabothrus argentum; Squamata: Boidae)

Abstract We present the complete mitochondrial genome of Chilabothrus argentum, which is 17,345 bp in length, has 22 transfer ribonucleic acids (tRNAs), 2 ribosomal subunits (rRNAs), 13 protein-coding genes, an origin of the light-strand replication (OL), and two control regions (CR1, CR2). A maximum likelihood phylogenetic estimate using nine other snake mitochondrial genomes yields agreement with previous investigations into the evolutionary relationships of snakes.
Identification and Safeguarding of Intangible Cultural Heritage (ICH) of Tabaco City, Philippines, through Cultural Mapping

Safeguarding intangible cultural heritage (ICH) includes identification, documentation, research, preservation, protection, promotion, enhancement, and transmission through formal and non-formal education. This paper identified the conditions and status of the intangible cultural heritage (ICH) of Tabaco City, Philippines, identified the threats to the preservation of ICH, and determined the measures taken to safeguard the ICH. It utilized purposive sampling and adopted the Cultural Mapping of the NCCA in the documentation and identification of ICH in Tabaco City that employed ethnography through community immersion and non-participant observation applying the Filipino psychology, focused group discussion, and unstructured interviews in data collection. Findings revealed that ICH in Tabaco City is under threat due to many ICH practitioners' death and old age, a rising lack of interest in ICH among the youth, and the influences of foreign culture. However, measures were put in place to safeguard ICH. The living practitioners of ICH in Tabaco City pass on their knowledge and traditional skills to the next generations by sustaining traditional blacksmithing, pedicab making, folk medicine, and native cuisines. Furthermore, ICH is promoted by generating awareness through various tourism activities. This paper also recommends the establishment of a school for living tradition that may teach the younger generations of the ICH in Tabaco City. Likewise, mainstreaming indigenous knowledge into the curriculum must be done so that the younger generations can derive valuable lessons from studying ICH. Lastly, the local government must adopt policies through local legislation to ensure the preservation, promotion, and protection of ICH and their living human treasures.
The Nexus between Causal Macroeconomic Relations in Japan

Japan achieved phenomenal economic growth after WWII. Starting in the early 1990s, however, the Japanese economy began experiencing a prolonged deflation-stagnation period widely known as the “Lost Decades”. Based on data from the World Bank and the Federal Reserve Bank of Saint Louis, this paper employs an autoregressive distributed lags (ARDL) model to find evidence of a long run relation among the real GDP, real imports, the real exchange rate, and the public debt-to-GDP ratio for Japan. Once cointegration is established with the Bounds Test, Granger Causality tests are performed by employing an estimated Vector Autoregressive (VAR) model with the same variables. The empirical results support Granger causality in all directions. In particular, we found real imports and public debt-to-GDP ratio to directly cause real GDP. Interestingly, the real exchange rate causes real GDP indirectly via imports. The public debt had a negative effect on GDP but did not wreak havoc on the Japanese economy. The study also examines whether former Prime Minister Shinzō Abe’s unprecedented macroeconomic policies and structural reforms launched in 2013, known as Abenomics, are pulling Japan out of its economic doldrums.
Effect of three-body interactions on Bénárd–von Kármán vortex street in quasi-2D Bose–Einstein condensate

The dynamical behaviors of a quasi-2D Bose–Einstein condensate (BEC) with three-body interactions through a moving obstacle potential are studied numerically. Various vortex structures are discovered under different strength of three-body interactions when the two-body interaction is attractive or repulsive. When the width and moving velocity of the obstacle potential reach critical value, periodic anti-symmetric double-row vortex pairs will be released alternately in BEC, and a Bénárd–von Kármán (BvK) vortex street will be formed eventually. We noticed that the BvK vortex street can be excited when the three-body interactions are taken into account even if the two-body interaction is attractive. The mean value of the distance between two vortex rows to the distance between two vortex pairs in the same row is about 0.2. It is slightly smaller than the stability condition 0.28 without considering the three-body interaction. The parameter regions of vortex patterns at different three-body interaction are determined. It is found that an appropriate value of three-body interaction with larger velocity and lesser width is favorable to the formation of BvK vortex street. In a pair of point vortices, the distance and angular velocity between them are nearly invariant while they rotate around their center. The internal rule of vortex pair are also analyzed by calculating the drag force acting on the obstacle potential. Finally, we proposed an experimental protocol to realize the vortex street in 87Rb BEC with three-body interactions.
Electrically tunable metasurfaces: from direct to indirect mechanisms

Intensive development of nanofabrication processes has opened a new window to control electromagnetic waves using subwavelength nanostructures array, named metasurfaces. Although the metasurfaces have succeeded in achieving unprecedented functionality by arranging various shapes of nanostructures to modulate the properties of the incident light, inherent passive characteristics make it impossible to alter the engraved functions after it is fabricated. To give tunability to metasurfaces, various methods have been proposed by using a thermal, chemical, optical and physical stimulus. In particular, electrically tunable metasurfaces are attractive in that they are easy to control precisely and could be integrated into electronic devices. In this review, we categorize the representative electrical tuning mechanisms and research into three; voltage-operated modulation, electrochemical-driven modulation, and externally mediated modulation. Voltage-operated modulation uses materials that could be directly reorganized by an electric field, including liquid crystals and Drude materials. Electrochemical-driven modulation adjusts the optical properties of metasurfaces through electrochemical responses such as electrochromism and electrodeposition. Lastly, externally mediated modulation causes a change in the geometric parameters of metasurfaces or in the phase of the constituent materials by converting electrical energy into thermal or mechanical stimulation. This paper concludes after explaining the pros and cons of each mechanism and the new possibilities which electrically-responsive metasurfaces could bring about.
Modeling the correlation between the workload and the power consumed by a server using stochastic and non‐parametric approaches

In this article, we address a critical concern of the growth of computer servers number and the resulting power expenditures in data centers by analyzing the statistical metrics related to the workload executed in each physical node. The aim is to build a stochastic model for power consumption estimation based on historical data. Relying on in‐depth investigation and experimental testing of the power consumption features and performance of the various workload datasets, we propose a model that considers the workload and the power consumed to be executed by a server as random variables. Based on the properties of the probabilistic distribution function of each random variable, we establish the correlation relationship between the workload and the power consumption using a non‐parametric approach. Our use of a non‐parametric method to learn a given probability model is challenging because it requires estimating the full distribution from the available data samples. The accuracy of our approach is demonstrated by estimating the energy consumption of various workloads. The experimental and simulation results show that our model outperforms many existing approaches in terms of accuracy, and it can be applied to a wide variety of workloads.
A comparative study of universal fuzzy logic and PI speed controllers for four switch BLDC motor drive

This paper presents a comparison of PI controller and fuzzy controller for speed control of a low cost brushless DC (BLDC) motor drive used in variable speed drive applications. The cost reduction in BLDC drive system is achieved by the reduction of power semiconductor switches. A PI controller and universal fuzzy controller are designed for the speed control and tested by simulation for various conditions. The simulation is performed by using MATLAB Simulink toolbox and the results show the effective response of the fuzzy controller. The rise time and steady state error of fuzzy controller is improved on an average of about 26% and 55% respectively compared to the conventional PI controller.
Using Facial Micro-Expressions in Combination With EEG and Physiological Signals for Emotion Recognition

Emotions are multimodal processes that play a crucial role in our everyday lives. Recognizing emotions is becoming more critical in a wide range of application domains such as healthcare, education, human-computer interaction, Virtual Reality, intelligent agents, entertainment, and more. Facial macro-expressions or intense facial expressions are the most common modalities in recognizing emotional states. However, since facial expressions can be voluntarily controlled, they may not accurately represent emotional states. Earlier studies have shown that facial micro-expressions are more reliable than facial macro-expressions for revealing emotions. They are subtle, involuntary movements responding to external stimuli that cannot be controlled. This paper proposes using facial micro-expressions combined with brain and physiological signals to more reliably detect underlying emotions. We describe our models for measuring arousal and valence levels from a combination of facial micro-expressions, Electroencephalography (EEG) signals, galvanic skin responses (GSR), and Photoplethysmography (PPG) signals. We then evaluate our model using the DEAP dataset and our own dataset based on a subject-independent approach. Lastly, we discuss our results, the limitations of our work, and how these limitations could be overcome. We also discuss future directions for using facial micro-expressions and physiological signals in emotion recognition.
Loss of Unconventional Myosin VI Affects cAMP/PKA Signaling in Hindlimb Skeletal Muscle in an Age-Dependent Manner

Myosin VI (MVI) is a unique unconventional myosin ubiquitously expressed in metazoans. Its diverse cellular functions are mediated by interactions with a number of binding partners present in multi-protein complexes. MVI is proposed to play important roles in muscle function and myogenesis. Previously, we showed that MVI is present in striated muscles and myogenic cells, and MVI interacts with A-kinase anchoring protein 9 (AKAP9), a scaffold for PKA and its regulatory proteins. Since PKA directly phosphorylates the MVI cargo binding domain, we hypothesized that the cellular effects of MVI are mediated by the cAMP/PKA signaling pathway, known to play important roles in skeletal muscle metabolism and myogenesis. To elucidate the potential role of MVI in PKA signaling in hindlimb muscle function, we used mice lacking MVI (Snell’s waltzer, SV), considered as natural MVI knockouts, and heterozygous littermates. We used muscles isolated from newborn (P0) as well as 3- and 12-month-old adult mice. We observed a significant increase in the muscle to body mass ratio, which was most evident for the soleus muscle, as well as changes in fiber size, indicating alterations in muscle metabolism. These observations were accompanied by age-dependent changes in the activity of PKA and cAMP/PKA-dependent transcriptional factor (CREB). Additionally, the levels of adenylate cyclase isoforms and phosphodiesterase (PDE4) were age-dependent. Also, cAMP levels were decreased in the muscle of P0 mice. Together, these observations indicate that lack of MVI impairs PKA signaling and results in the observed alterations in the SV muscle metabolism, in particular in newborn mice.
Influence of composite admixtures on the freezing resistance and pore structure characteristics of cemented sand and gravel

CSG shows low freezing resistance due to its poor cementing properties, and adding admixtures is the main method to improve its freezing resistance. In this paper, experiments were designed based on the Taguchi method and on the freezing resistance and pore structure characteristics of CSG after adding admixtures. After 20 freeze-thaw cycles, the results showed that both the compressive strength and dynamic elastic modulus of CSG decreased. The area occupancy of more harmful pores (>200 nm) in the NMR test increased, and the fractal dimension of the pore structure in the SEM images also increased. Based on the above indicators, it was concluded that L9 (sodium dodecyl sulfate, graphene oxide, nanosilica powder, and water-reducing agent) has the best freezing resistance. Experiments were conducted on L9 to verify that L9 can significantly improve the freezing resistance of CSG materials under different water-binder ratios, sand ratios, and cement dosages.
Application of ANN in Induction-Motor Fault-Detection System Established with MRA and CFFS

This paper proposes a fault-detection system for faulty induction motors (bearing faults, interturn shorts, and broken rotor bars) based on multiresolution analysis (MRA), correlation and fitness values-based feature selection (CFFS), and artificial neural network (ANN). First, this study compares two feature-extraction methods: the MRA and the Hilbert Huang transform (HHT) for induction-motor-current signature analysis. Furthermore, feature-selection methods are compared to reduce the number of features and maintain the best accuracy of the detection system to lower operating costs. Finally, the proposed detection system is tested with additive white Gaussian noise, and the signal-processing method and feature-selection method with good performance are selected to establish the best detection system. According to the results, features extracted from MRA can achieve better performance than HHT using CFFS and ANN. In the proposed detection system, CFFS significantly reduces the operation cost (95% of the number of features) and maintains 93% accuracy using ANN.
Machine-Learning-Based Digital Twin in Manufacturing: A Bibliometric Analysis and Evolutionary Overview

The Digital Twin (DT) concept in the manufacturing industry has received considerable attention from researchers because of its versatile application potential. Machine Learning (ML) adds a new dimension to DT by enhancing its functionality. Many studies on DT in the manufacturing industry have recently been published. However, there is still a lack of a systematic literature review on different aspects of ML-based DT in the manufacturing industry from a bibliometric and evolutionary perspective. Therefore, the proposed study is mainly aimed at reviewing DT in the manufacturing industry to identify the contribution of ML, current methods, and future research directions. According to the findings, the contribution of ML to this domain is significant. Additionally, the results show that the latest ML technologies are being used in the DT domain; neural networks have evolved based on application-specific requirements. The total number of papers and citations per paper on ML-based DT is increasing. The relevance of ML in DT has increased over time. The current trend is to use ML-based DT for data analytics. Additionally, there are many unfilled gaps; certain gaps include industrial applications of DT, synchronisation with real-time data through sensors, heterogeneous data management, and benchmarking.