Miracle-dz/UniFAR-FTUs huggingface dataset:
Human Stem Cell-Derived TRPV1-Positive Sensory Neurons: A New Tool to Study Mechanisms of Sensitization

Somatosensation, the detection and transduction of external and internal stimuli such as temperature or mechanical force, is vital to sustaining our bodily integrity. But still, some of the mechanisms of distinct stimuli detection and transduction are not entirely understood, especially when noxious perception turns into chronic pain. Over the past decade major progress has increased our understanding in areas such as mechanotransduction or sensory neuron classification. However, it is in particular the access to human pluripotent stem cells and the possibility of generating and studying human sensory neurons that has enriched the somatosensory research field. Based on our previous work, we describe here the generation of human stem cell-derived nociceptor-like cells. We show that by varying the differentiation strategy, we can produce different nociceptive subpopulations with different responsiveness to nociceptive stimuli such as capsaicin. Functional as well as deep sequencing analysis demonstrated that one protocol in particular allowed the generation of a mechano-nociceptive sensory neuron population, homogeneously expressing TRPV1. Accordingly, we find the cells to homogenously respond to capsaicin, to become sensitized upon inflammatory stimuli, and to respond to temperature stimulation. The efficient and homogenous generation of these neurons make them an ideal translational tool to study mechanisms of sensitization, also in the context of chronic pain.
Constraining the location of rapid gamma-ray flares in the FSRQ 3C 273

We present a \gamma-ray photon flux and spectral variability study of the flat-spectrum radio quasar 3C 273 over a rapid flaring activity period between September 2009 to April 2010. Five major flares are observed in the source during this period. The most rapid flare observed in the source has a flux doubling time of 1.1 hr. The rapid \gamma-ray flares allow us to constrain the location and size of the \gamma-ray emission region in the source. The \gamma \gamma-opacity constrains the Doppler factor, $\delta_{\gamma} \geq$ 10 for the highest energy (15 GeV) photon observed by the {\it Fermi}-Large Area Telescope (LAT). Causality arguments constrain the size of the emission region to 1.6$\times 10^{15}$ cm. The \gamma-ray spectra measured over this period show clear deviations from a simple power law with a break in 1-2 GeV energy range. We discuss possible explanations for the origin of the \gamma-ray spectral breaks. Our study suggests that the \gamma-ray emission region in 3C 273 is located within the broad line region ($<$1.6 pc). The spectral behavior and temporal characteristics of the individual flares indicate the presence of multiple shock scenarios at the base of the jet.
Evaluation of Geometric Attractor Structure and Recurrence Analysis in Professional Dancers

Background: Human motor systems contain nonlinear features. The purpose of this study was to evaluate the geometric structure of attractors and analyze recurrence in two different pirouettes (jazz and classic) performed by 15 professional dancers. Methods: The kinematics of the body’s center of mass (CoM) and knee of the supporting leg (LKNE) during the pirouette were measured using the Vicon system. A time series of selected points were resampled, normalized, and randomly reordered. Then, every second time series was flipped to be combined with other time series and make a long time series out of the repetitions of a single task. The attractors were reconstructed, and the convex hull volumes (CHV) were counted for the CoM and LKNE for each pirouette in each direction. Recurrence quantification analysis (RQA) was used to extract additional information. Results: The CHVs calculated for the LKNE were significantly lower for the jazz pirouette. All RQA measures had the highest values for LKNE along the mediolateral axis for the jazz pirouette. This result underscores the high determinism, high motion recurrence, and complexity of this maneuver. Conclusions: The findings offer new insight into the evaluation of the approximation of homogeneity in motion control. A high determinism indicates a highly stable and predictive motion trajectory.
Magnetic 3D-Printed Composites—Production and Applications

Three-dimensional printing enables building objects shaped with a large degree of freedom. Additional functionalities can be included by modifying the printing material, e.g., by embedding nanoparticles in the molten polymer feedstock, the resin, or the solution used for printing, respectively. Such composite materials may be stronger or more flexible, conductive, magnetic, etc. Here, we give an overview of magnetic composites, 3D-printed by different techniques, and their potential applications. The production of the feedstock is described as well as the influence of printing parameters on the magnetic and mechanical properties of such polymer/magnetic composites.
Study of spectroscopic analysis performed of laser produced Sn and Sn:Cr plasma

In this study, the LIBS technique was used to generate plasma from the Sn and Sn:Cr surfaces of these nanomaterials and to quantify the spectral emission of tin and chromium-containing tin. Nd-YAG laser has a wavelength of 1064 nm and a frequency of 6 Hz. After computing the plasma parameters, it was determined that the electron temperature is plasma Sn(1.286–1.819)ev and Sn: Cr (1.661-2.11505922)ev. The Sn(1.65-2.40) 108 cm -3 plasma electron density was also detected. Plasma Sn:Cr (2.85-3.39) 108cm-3 Additionally, calculations were made for plasma properties such plasma frequency, Debye length, and Debye number. At atmospheric pressure, the spectrum range of laser-induced plasma emission (200-1100 nm) was determined.
STEM Faculty’s Support of Togetherness during Mandated Separation: Accommodations, Caring, Crisis Management, and Powerlessness

The emergence of the COVID-19 pandemic initiated major disruptions to higher education systems. Physical spaces that previously supported interpersonal interaction and community were abruptly inactivated, and faculty largely took on the responsibility of accommodating classroom structures in rapidly changing situations. This study employed interviews to examine how undergraduate Science, Technology, Engineering, and Mathematics (STEM) instructors adapted instruction to accommodate the mandated transition to virtual learning and how these accommodations supported or hindered community and belonging during the onset of the pandemic. Interviews with 25 STEM faculty at an undergraduate Hispanic Serving Institution revealed a wide range of accommodations they made to their courses and how they managed communication with students. Faculty strived to support student belonging with responses ranging from caring to crisis management, though some faculty expressed feelings of powerlessness when unable to accommodate certain challenges. The case of a responsive and flexible instructor is presented to highlight a productive response to a crisis. These retrospective findings point to strategies to support faculty teaching in virtual learning environments in the future; increasing opportunities for student–student and student–faculty interaction, supporting faculty in learning technologies that support these interactions and addressing faculty’s feelings of powerlessness.
Insights on KP4 Killer Toxin-like Proteins of Fusarium Species in Interspecific Interactions

KP4 killer toxins are secreted proteins that inhibit cell growth and induce cell death in target organisms. In Fusarium graminearum, KP4-like (KP4L) proteins contribute to fungal virulence in wheat seedling rot and are expressed during Fusarium head blight development. However, fungal KP4L proteins are also hypothesized to support fungal antagonism by permeabilizing cell walls of competing fungi to enable penetration of toxic compounds. Here, we report the differential expression patterns of F. graminearum KP4L genes (Fgkp4l-1, -2, -3 and -4) in a competitive interaction, using Trichoderma gamsii as the antagonist. The results from dual cultures indicate that Fgkp4l-3 and Fgkp4l-4 could participate in the recognition at the distance of the antagonist, while all Fgkp4l genes were highly activated in the pathogen during the physical interaction of both fungi. Only Fgkp4l-4 was up-regulated during the interaction with T. gamsii in wheat spikes. This suggests the KP4L proteins could participate in supporting F. graminearum interspecific interactions, even in living plant tissues. The distribution of KP4L orthologous within the genus Fusarium revealed they are more represented in species with broad host-plant range than in host-specific species. Phylogeny inferred provides evidence that KP4L genes evolved through gene duplications, gene loss and sequence diversification in the genus Fusarium.
Dynamic Balance and Chest Mobility of Older Adults after Speleotherapy Combined with Pulmonary Rehabilitation, Endurance and Strength Training—A Prospective Study in Chronic Respiratory Diseases

Backgrounds: As people age, they are more likely to experience balance disturbances. Pulmonary rehabilitation is recognized as a core component in the management of older adults with chronic respiratory diseases. The aim of the study was to assess the dynamic balance and chest mobility of older adults participating in speleotherapy combined with pulmonary rehabilitation, endurance and strength training. Methods: The study group consisted of 51 older adults with chronic respiratory disorders who participated in a 3-week pulmonary rehabilitation programme in underground salt chambers in the ‘Wieliczka’ Salt Mine Health Resort. These individuals underwent the Four Square Step Test (FSST) and circumferential chest mobility measurement before and after the outpatient rehabilitation programme conducted 135 m underground. Results: Before rehabilitation in the underground salt chambers, half of the results (50%, 22 patients) were below the norm in the assessment of chest mobility between maximal inhale and exhale. The average time needed to perform FSST decreased significantly (p ≤ 0.05) from 10.2 ± 1.9 s before the stay to 9.1 ± 1.7 s after the stay and the average increase in chest mobility increased significantly (p ≤ 0.05) from 4.5 ± 5.5 cm to 5.4 ± 2.8 cm. Conclusions: Speleotherapy combined with pulmonary rehabilitation, endurance and strength training increased the dynamic balance and chest mobility of older adults with chronic respiratory diseases, as measured by the FSST and circumferential chest expansion assessment.
Human Resource Management Using Machine Learning-Based Solutions

Human resource management (HRM) is more like a strategic alignment with the purpose. AI (Artificial Intelligence) based techniques are becoming increasingly significant in HRM operations. The firms are concentrating on more realistic solutions. ML (Machine learning) algorithms are currently making advances in a range of human resource management operations. This report gives an overview of critical HRM operational functions where AI-based solutions in particular ML techniques may be applied to improve accuracy of HRM operational processes. This paper addresses unique possible and probable opportunity of AI techniques, and focuses on features noted as (i) engaging employees, (ii) corporate culture administration, (iii) and the evaluation. The employment of ML techniques decision tree as well as logistic regression to enhance the possibility of solutions being more substantial as well as capable of offering the ideal evaluation process. If solutions are created along these lines, they can be advantageous to firms in terms of managing strategic HRM operational practices.
An Improved Method of Image Recognition with Deep Learning Combined with Attention Mechanism

An improved convolutional neural network (CNN) recognition model is proposed for the problems involving low recognition rate and weak generalization ability for flower images. Highly abstracted features after multiple convolutions are integrated, and the performance of network is improved by adding the network model for multi-attention mechanism after residual module for Inception-resnet-V2 Network and fully connected layer before activating the function. The improved model is simulated by integrating OxFlowers 17 and Oxford 102 flower data sets. The results show that the recognition rate of the model based on Inception-resnet-V2 Network combined with attention mechanism is up to 97.6%, being 5.1% higher than that of the original model, and the accuracy for flowers recognition is improved significantly.
Downregulation of LKB1/AMPK Signaling in Blood Mononuclear Cells Is Associated with the Severity of Guillain–Barre Syndrome

AMP-activated protein kinase (AMPK) is an intracellular energy sensor that regulates metabolic and immune functions mainly through the inhibition of the mechanistic target of rapamycin (mTOR)-dependent anabolic pathways and the activation of catabolic processes such as autophagy. The AMPK/mTOR signaling pathway and autophagy markers were analyzed by immunoblotting in blood mononuclear cells of 20 healthy control subjects and 23 patients with an acute demyelinating form of Guillain–Barré syndrome (GBS). The activation of the liver kinase B1 (LKB1)/AMPK/Raptor signaling axis was significantly reduced in GBS compared to control subjects. In contrast, the phosphorylated forms of mTOR activator AKT and mTOR substrate 4EBP1, as well as the levels of autophagy markers LC3-II, beclin-1, ATG5, p62/sequestosome 1, and NBR1 were similar between the two groups. The downregulation of LKB1/AMPK signaling, but not the activation status of the AKT/mTOR/4EBP1 pathway or the levels of autophagy markers, correlated with higher clinical activity and worse outcomes of GBS. A retrospective study in a diabetic cohort of GBS patients demonstrated that treatment with AMPK activator metformin was associated with milder GBS compared to insulin/sulphonylurea therapy. In conclusion, the impairment of the LKB1/AMPK pathway might contribute to the development/progression of GBS, thus representing a potential therapeutic target in this immune-mediated peripheral polyneuropathy.
Explainable AI for Time Series Classification: A Review, Taxonomy and Research Directions

Time series data is increasingly used in a wide range of fields, and it is often relied on in crucial applications and high-stakes decision-making. For instance, sensors generate time series data to recognize different types of anomalies through automatic decision-making systems. Typically, these systems are realized with machine learning models that achieve top-tier performance on time series classification tasks. Unfortunately, the logic behind their prediction is opaque and hard to understand from a human standpoint. Recently, we observed a consistent increase in the development of explanation methods for time series classification justifying the need to structure and review the field. In this work, we (a) present the first extensive literature review on Explainable AI (XAI) for time series classification, (b) categorize the research field through a taxonomy subdividing the methods into time points-based, subsequences-based and instance-based, and (c) identify open research directions regarding the type of explanations and the evaluation of explanations and interpretability.
Dynamic Malicious Code Detection Based on Improved Graph Embedding Model

With the continuous development of science and technology, the generation of malicious code is becoming much easier than ever before, and various variants emerge in endlessly, which makes the detection of malicious code increasingly difficult and causes irreversible losses to users’ systems, information, and assets. Aiming at this problem, this paper proposes an intelligent malicious code detection method based on an improved graph embedding model. The method uses heterogeneous information network to describe the malicious code dynamic features API, DLL, FILE, uses meta-graph and CBOW-based improved graph embedding model in the embedding processing part of the network, and finally multiple meta-graph features are comprehensively classified by SVM classification network and policy voting method, and the classification results are obtained. The experiment uses data sets collected from various channels for training and verification, and the classification accuracy rate reaches 97.0%, and the false positive rate is only 0.3%. Compared with other methods’ performance analysis, this method has better accuracy and precision in malicious code detection, and is better than other existing methods.
Expectation of absolute difference of median filters

The expectation of the absolute difference between the input and output signals of median filters is studied. The expectation of the absolute difference is an estimate of how much a filter changes a signal, and can therefore be used to quantify the detail preservation ability of a filter. A method for calculating the expectation of the absolute difference is reviewed. The method is used to calculate the expectation of the absolute difference in the case of median filters with varying window sizes. An elegant formula is derived for the expectation of the absolute difference of a median filter in the case of the uniform distribution. The considered input distributions also include the Gaussian and the Laplacian (biexponential) distributions. The asymptotic values of the expectation of the absolute difference are also derived for the three above mentioned input distributions when the size of the filter window approaches infinity.
Anchor management: a field experiment to encourage families to meet critical programme deadlines

Abstract Many families, despite need and eligibility, struggle to meet programme deadlines to retain critical benefits. When families fail to complete programme recertification on time, they lose needed support. While scholars have tested behavioural theories like chunking, implementation intention, and loss framing to promote programme uptake, less is known about how well-designed communications can promote continuity through successful recertification, especially where recertification entails a significant administrative burden. Further, scant evidence guides how best to frame recertification deadlines. In a randomised trial with government partners (n = 3,539), we find that sending a reminder letter informed by these behavioural theories increased the number of families maintaining participation by 14 per cent. Further, anchoring people to a deadline month may suffice to thread the motivational needle: overcoming procrastination without lowering self-efficacy by anchoring them to a specific day. Adopting the most effective letter in Washington, DC, would lead 766 more families to participate uninterrupted each year.
Phase retrieval using iterative Fourier transform and convex optimization algorithm

Phase is an inherent characteristic of any wave field. Statistics show that greater than 25% of the information is encoded in the amplitude term and 75% of the information is in the phase term. The technique of phase retrieval means acquire phase by computation using magnitude measurements and provides data information for holography display, 3D field reconstruction, X-ray crystallography, diffraction imaging, astronomical imaging and many other applications. Mathematically, solving phase retrieval problem is an inverse problem taking the physical and computation constraints. Some recent algorithms use the principle of compressive sensing, such as PhaseLift, PhaseCut and compressive phase retrieval etc. they formulate phase retrieval problems as one of finding the rank-one solution to a system of linear matrix equations and make the overall algorithm a convex program over n × n matrices. However, by "lifting" a vector problem to a matrix one, these methods lead to a much higher computational cost as a result. Furthermore, they only use intensity measurements but few physical constraints. In the paper, a new algorithm is proposed that combines above convex optimization methods with a well known iterative Fourier transform algorithm (IFTA). The IFTA iterates between the object domain and spectral domain to reinforce the physical information and reaches convergence quickly which has been proved in many applications such as compute-generated-hologram (CGH). Herein the output phase of the IFTA is treated as the initial guess of convex optimization methods, and then the reconstructed phase is numerically computed by using modified TFOCS. Simulation results show that the combined algorithm increases the likelihood of successful recovery as well as improves the precision of solution.
Variation in Phenolic Profile, Antioxidant, and Anti-Inflammatory Activities of Salvadora oleoides Decene. and Salvadora persica L. Fruits and Aerial Part Extracts

(1) Background: The objective of this study was to investigate the potential of Salvadora oleoides (S. oleoides) and Salvadora persica (S. persica) polyphenols as antioxidant and anti-inflammatory agents. (2) Methods: Aerial parts and fruits of S. oleoides and S. persica were collected from the periphery of District Bhakkar, Punjab, Pakistan. Methanol extracts were prepared using the Soxhlet extraction technique. Extract yield varied from 8.15 to 19.6 g/100 g dry plant material. RP-HPLC revealed the detection of thirteen phenolic aids and five flavonoids. Gallic acid, hydroxy benzoic acid, chlorogenic acid, and cinamic acid were the major phenolic acids, whereas catechin, rutin, and myricetin were the flavonoids detected. (3) Results: Maximum total phenolic contents (TPCs) (22.2 mg/g of dry plant material) and total flavonoid contents (TFCs) (6.17 mg/g of dry plant material) were found in the fruit extract of S. persica, and the minimum TPC (11.9 mg/g) and TFC (1.72 mg/g) were found in the aerial part of S. oleoides. The fruit extract of S. persica showed the highest DPPH radical scavenging activity. In vivo anti-inflammatory activity of all the extracts was performed on albumin-induced rat paw edema that was comparable with the standard indomethacin; S. persica fruit extract showed remarkable anti-inflammatory activity. Analgesic activity of aerial part and fruit extracts of S. oleoides and S. persica was investigated using a mouse model, and the results showed that maximum possible analgesia of fruit extracts of S. persica was 53.44%, which is better than the PC group (52.98%). (4) Conclusions: The variations in the antioxidant, anti-inflammatory, and analgesic activities of methanolic extracts of S. oleoides and S. persica were found to be significant, and they have therapeutic potential as antioxidant, analgesic, and anti-inflammatory agents.
Cross Layer Design for Primary User Modeling in Cognitive Radio Ad-hoc Sensor Network

Presence of Primary Users is one of the critical roadblocks for successful operation among SU network. Increasing primary users’ activity in the available spectrum band, enhances the chances of interference and spectrum unavailability for Secondary Users (SU) communication. All the existing techniques consider the fact that, at a time, only single Primary User (PU) is using the license spectrum band. But this assumption is not correct for real-time scenarios, as there can be multiple PUs operating simultaneously, further degrading the SU performance. Hence, all the interference calculation, channel switching and spectrum access design related models considering single PU operation, do not make practical sense for real time deployment of cognitive radio sensor network. Therefore, the concept of multiple PU operation (CogRPU) has been proposed by considering the real time field deployment scenario. Furthermore, a suitable cross layer design is developed in order to overcome the CogRPU problem and it showed a considerable performance improvement between without cross layer and with cross layer techniques.