Miracle-dz/UniFAR-FTUs huggingface dataset:
The Size of the Broad-Line Region in the Seyfert Galaxy NGC 4151

Cross-correlation analysis of broad emission-line and optical continuum fluxes obtained from new spectro-photometric observations of NGC 4151 is used to estimate the size of the broad-line region (BLR) in this galaxy. The size of the BLR is found to be about 6 lt-days, though this may easily be in error by a factor of two. While the uncertainty is rather large, it is emphasized that this is a considerable improvement over previous determinations in which the magnitude of the uncertainties was not fully appreciated. It is also pointed out that there is insufficient evidence to support earlier claims that the different BLR radii inferred from cross-correlation of different broad lines with the continuum provide evidence for ionization stratification within the BLR. 39 references.
Measurement Property Evaluation of the Arabic Version of the Patient-Specific Functional Scale for Patients with Stroke

Neurological disorders refer to disorders that occur due to disease or damage to the nervous system. Stroke is one of the most common neurological disorders in which individuals commonly present with motor and sensory deficits, leading to the limitations on the activities of daily life. Outcome measures are used to assess and monitor patients’ condition change. The patient-specific functional scale (PSFS) is an outcome measure used to assess changes in performance levels in participants with a functional disability during daily activities. This study aimed to assess the reliability and validity of the Arabic version of the patient-specific functional scale (PSFS-Ar) in individuals with stroke. A longitudinal cohort study was used to examine the reliability and validity of the PSFS-Ar in patients with stroke. All participants completed the PSFS-Ar in addition to other outcome measures. Fifty-five individuals participated (fifty male, five female). The PSFS-Ar showed excellent test–retest reliability, with ICC2,1 = 0.96, p < 0.001. The SEM and MDC95 of the PSFS-Ar were 0.37 and 1.03, respectively. No floor and ceiling effect was observed in this study. Additionally, the construct validity of the PSFS-Ar showed 100% satisfaction with the pre-defined hypotheses. Since the number of female participants was very small in this study, the findings were established for male individuals with stroke. This study showed that the PSFS-Ar is a reliable and valid outcome measure for male individuals with stroke.
Comparison of Estetrol Exposure between Women and Mice to Model Preclinical Experiments and Anticipate Human Treatment

Estetrol (E4) is a natural estrogen with promising therapeutic applications in humans. The European Medicines Agency and the Food and Drug Administration have approved the use of 15 mg E4/3 mg drospirenone for contraceptive indication. Phase III clinical trials with 15–20 mg E4 for the relief of climacteric complaints are currently running. Relevant data from preclinical animal models are needed to characterize the molecular mechanisms and the pharmacological effects of E4 and possibly to reveal new therapeutic applications and to anticipate potential adverse effects. Therefore, it is important to design experimental procedures in rodents that closely mimic or anticipate human E4 exposure. In this study, we compared the effects of E4 exposure after acute or chronic administration in women and mice. Women who received chronic E4 treatment per os at a dose of 15 mg once daily reached a steady state within 6 to 8 days, with a mean plasma concentration of 3.20 ng/mL. Importantly, with subcutaneous, intraperitoneal or oral administration of E4 in mice, a stable concentration over time that would mimic human pharmacokinetics could not be achieved. The use of osmotic minipumps continuously releasing E4 for several weeks provided an exposure profile mimicking chronic oral administration in women. Measurements of the circulating concentration of E4 in mice revealed that the mouse equivalent dose necessary to mimic human treatment does not fit with the allometric prediction. In conclusion, this study highlights the importance of precise definition of the most appropriate dose and route of administration to utilize when developing predictive preclinical animal models to mimic or anticipate specific human treatment.
Evaluation of the Mechanical, Thermal and Rheological Properties of Hop, Hemp and Wood Fiber Plastic Composites

The aim of this study was to evaluate the use of waste natural fibers from milled hop bines and hemp stalks, without chemical treatment, and compare them to a commercial wood fiber for use in wood–plastic composite (WPC) materials. The fibers were characterized (density, fiber size and chemical composition). WPCs were produced by the extrusion of a blend of fibers (50%), high-density polyethylene (HDPE) and coupling agent (2%). The WPCs were characterized for their mechanical, rheological, thermal, viscoelastic and water resistance properties. Pine fiber was about half the size of hemp and hop fibers and thus had a higher surface area. The pine WPC melts had a higher viscosity than the other two WPCs. Additionally, the tensile and flexural strengths of the pine WPC were higher than those of hop and hemp WPCs. The pine WPC was also shown to have the least water absorption followed by hop and hemp WPCs. This study highlights that different lignocellulosic fibers influence their WPC properties. The properties of the hop- and hemp-based WPCs were comparable to commercial WPCs and can be improved by further milling/screening the fibers to a smaller particle size (volumetric mean of ~88 μm) to increase their surface area, fiber–matrix interactions and improve stress-transfer.
Unsupervised Monocular Depth and Camera Pose Estimation with Multiple Masks and Geometric Consistency Constraints

This paper presents a novel unsupervised learning framework for estimating scene depth and camera pose from video sequences, fundamental to many high-level tasks such as 3D reconstruction, visual navigation, and augmented reality. Although existing unsupervised methods have achieved promising results, their performance suffers in challenging scenes such as those with dynamic objects and occluded regions. As a result, multiple mask technologies and geometric consistency constraints are adopted in this research to mitigate their negative impacts. Firstly, multiple mask technologies are used to identify numerous outliers in the scene, which are excluded from the loss computation. In addition, the identified outliers are employed as a supervised signal to train a mask estimation network. The estimated mask is then utilized to preprocess the input to the pose estimation network, mitigating the potential adverse effects of challenging scenes on pose estimation. Furthermore, we propose geometric consistency constraints to reduce the sensitivity of illumination changes, which act as additional supervised signals to train the network. Experimental results on the KITTI dataset demonstrate that our proposed strategies can effectively enhance the model’s performance, outperforming other unsupervised methods.
Improved Loading Capacity and Viability of Probiotics Encapsulated in Alginate Hydrogel Beads by In Situ Cultivation Method

The objective of this research was to encapsulate probiotics by alginate hydrogel beads based on an in situ cultivation method and investigate the influences on the cell loading capacity, surface and internal structure of hydrogel beads and in vitro gastrointestinal digestion property of cells. Hydrogel beads were prepared by extrusion and cultured in MRS broth to allow probiotics to grow inside. Up to 10.34 ± 0.02 Log CFU/g of viable cell concentration was obtained after 24 h of in situ cultivation, which broke through the bottleneck of low viable cell counts in the traditional extrusion method. Morphology and rheological analyses showed that the structure of the eventually formed probiotic hydrogel beads can be loosed by the existence of hydrogen bond interaction with water molecules and the internal growth of probiotic microcolonies, while it can be tightened by the acids metabolized by the probiotic bacteria during cultivation. In vitro gastrointestinal digestion analysis showed that great improvement with only 1.09 Log CFU/g of loss in viable cells was found after the entire 6 h of digestion. In conclusion, the current study demonstrated that probiotic microcapsules fabricated by in situ cultivation method have the advantages of both high loading capacity of encapsulated viable cells and good protection during gastrointestinal digestion.
The Aging Enteric Nervous System

The gut and the brain communicate via the nervous system, hormones, microbiota-mediated substances, and the immune system. These intricate interactions have led to the term “gut-brain axis”. Unlike the brain—which is somewhat protected—the gut is exposed to a variety of factors throughout life and, consequently, might be either more vulnerable or better adapted to respond to these challenges. Alterations in gut function are common in the elder population and associated with many human pathologies, including neurodegenerative diseases. Different studies suggest that changes in the nervous system of the gut, the enteric nervous system (ENS), during aging may result in gastrointestinal dysfunction and initiate human pathologies of the brain via its interconnection with the gut. This review aims at summarizing the contribution of normal cellular aging to the age-associated physiological changes of the ENS. Morphological alterations and degeneration of the aging ENS are observed in different animal models and humans, albeit with considerable variability. The aging phenotypes and pathophysiological mechanisms of the aging ENS have highlighted the involvement of enteric neurons in age-related diseases of the central nervous system such as Alzheimer’s or Parkinson’s disease. To further elucidate such mechanisms, the ENS constitutes a promising source of material for diagnosis and therapeutic predictions, as it is more accessible than the brain.
Mind the Curve: Dose–Response Fitting Biases the Synergy Scores across Software Used for Chemotherapy Combination Studies

Drug combinations are increasingly studied in the field of anticancer agents. Mathematical models, such as Loewe, Bliss, and HSA, are used to interpret drug combinations, while informatics tools help cancer researchers identify the most effective combinations. However, the different algorithms each software uses lead to results that do not always correlate. This study compared the performance of Combenefit (Ver. 2.021) and SynergyFinder (Ver. 3.6) in analyzing drug synergy by studying combinations involving non-steroidal analgesics (celecoxib and indomethacin) and antitumor drugs (carboplatin, gemcitabine, and vinorelbine) on two canine mammary tumor cell lines. The drugs were characterized, their optimal concentration–response ranges were determined, and nine concentrations of each drug were used to make combination matrices. Viability data were analyzed under the HSA, Loewe, and Bliss models. Celecoxib-based combinations showed the most consistent synergistic effect among software and reference models. Combination heatmaps revealed that Combenefit gave stronger synergy signals, while SynergyFinder produced better concentration–response fitting. When the average values of the combination matrices were compared, some combinations shifted from synergistic to antagonistic due to differences in the curve fitting. We also used a simulated dataset to normalize each software’s synergy scores, finding that Combenefit tends to increase the distance between synergistic and antagonistic combinations. We conclude that concentration–response data fitting biases the direction of the combination (synergistic or antagonistic). In contrast, the scoring from each software increases the differences among synergistic or antagonistic combinations in Combenefit when compared to SynergyFinder. We strongly recommend using multiple reference models and reporting complete data analysis for synergy claiming in combination studies.
Effect of Achillea fragrantissima Extract on Excision Wound Biofilms of MRSA and Pseudomonas aeruginosa in Diabetic Mice

Achillea fragrantissima, a desert plant commonly known as yarrow, is traditionally used as an antimicrobial agent in folklore medicine in Saudi Arabia. The current study was undertaken to determine its antibiofilm activity against methicillin-resistant Staphylococcus aureus (MRSA) and multi-drug-resistant Pseudomonas aeruginosa (MDR-P. aeruginosa) using in vitro and in vivo studies. A biofilm model induced through an excision wound in diabetic mice was used to evaluate its effect in vivo. The skin irritation and cytotoxic effects of the extract were determined using mice and HaCaT cell lines, respectively. The Achillea fragrantissima methanolic extract was analyzed with LC-MS to detect different phytoconstituents, which revealed the presence of 47 different phytoconstituents. The extract inhibited the growth of both tested pathogens in vitro. It also increased the healing of biofilm-formed excision wounds, demonstrating its antibiofilm, antimicrobial, and wound-healing action in vivo. The effect of the extract was concentration-dependent, and its activity was stronger against MRSA than MDR-P. aeruginosa. The extract formulation was devoid of a skin irritation effect in vivo and cytotoxic effect on HaCaT cell lines in vitro.
NOTES AND CORRESPONDENCE Inertial Particle Dynamics in a Hurricane

The motion of inertial (i.e., finite-size) particles is analyzed in a three-dimensional unsteady simulation of Hurricane Isabel. As established recently, the long-term dynamics of inertial particles in a fluid is governed by a reduced-order inertial equation, obtained as a small perturbation of passive fluid advection on a globally attracting slow manifold in the phase space of particle motions. Use of the inertial equation enables the visualization of three-dimensional inertial Lagrangian coherent structures (ILCS) on the slow manifold. These ILCS govern the asymptotic behavior of finite-size particles within a hurricane. A comparison of the attracting ILCS with conventional Eulerian fields reveals the Lagrangian footprint of the hurricane eyewall and of a large rainband. By contrast, repelling ILCS within the eye region admit a more complex geometry that cannot be compared directly with Eulerian features.
A Support Vector Machine-Based Approach for Bolt Loosening Monitoring in Industrial Customized Vehicles

Machine learning techniques have progressively emerged as important and reliable tools that, when combined with machine condition monitoring, can diagnose faults with even superior performance than other condition-based monitoring approaches. Furthermore, statistical or model-based approaches are often not applicable in industrial environments with a high degree of customization of equipment and machines. Structures such as bolted joints are a key part of the industry; therefore, monitoring their health is critical to maintaining structural integrity. Despite this, there has been little research on the detection of bolt loosening in rotating joints. In this study, vibration-based detection of bolt loosening in a rotating joint of a custom sewer cleaning vehicle transmission was performed using support vector machines (SVM). Different failures were analyzed for various vehicle operating conditions. Several classifiers were trained to evaluate the influence of the number and location of accelerometers used and to determine the best approach between specific models for each operating condition or a single model for all cases. The results showed that using a single SVM model with data from four accelerometers mounted both upstream and downstream of the bolted joint resulted in more reliable fault detection, with an overall accuracy of 92.4%.
HPIP is an essential scaffolding protein running through the EGFR-RAS-ERK pathway and drives tumorigenesis

The EGFR-RAS-ERK pathway plays a key role in cancer development and progression. However, the integral assembly of EGFR-RAS-ERK signaling complexes from the upstream component EGFR to the downstream component ERK is largely unknown. Here, we show that hematopoietic PBX-interacting protein (HPIP) interacts with all classical components of the EGFR-RAS-ERK pathway and forms at least two complexes with overlapping components. Experiments of HPIP knockout or knockdown and chemical inhibition of HPIP expression showed that HPIP is required for EGFR-RAS-ERK signaling complex formation, EGFR-RAS-ERK signaling activation, and EGFR-RAS-ERK signaling–mediated promotion of aerobic glycolysis as well as cancer cell growth in vitro and in vivo. HPIP expression is correlated with EGFR-RAS-ERK signaling activation and predicts worse clinical outcomes in patients with lung cancer. These results provide insights into EGFR-RAS-ERK signaling complex formation and EGFR-RAS-ERK signaling regulation and suggest that HPIP may be a promising therapeutic target for cancer with dysregulated EGFR-RAS-ERK signaling.
Regulators of the Asexual Life Cycle of Aspergillus nidulans

The genus Aspergillus, one of the most abundant airborne fungi, is classified into hundreds of species that affect humans, animals, and plants. Among these, Aspergillus nidulans, as a key model organism, has been extensively studied to understand the mechanisms governing growth and development, physiology, and gene regulation in fungi. A. nidulans primarily reproduces by forming millions of asexual spores known as conidia. The asexual life cycle of A. nidulans can be simply divided into growth and asexual development (conidiation). After a certain period of vegetative growth, some vegetative cells (hyphae) develop into specialized asexual structures called conidiophores. Each A. nidulans conidiophore is composed of a foot cell, stalk, vesicle, metulae, phialides, and 12,000 conidia. This vegetative-to-developmental transition requires the activity of various regulators including FLB proteins, BrlA, and AbaA. Asymmetric repetitive mitotic cell division of phialides results in the formation of immature conidia. Subsequent conidial maturation requires multiple regulators such as WetA, VosA, and VelB. Matured conidia maintain cellular integrity and long-term viability against various stresses and desiccation. Under appropriate conditions, the resting conidia germinate and form new colonies, and this process is governed by a myriad of regulators, such as CreA and SocA. To date, a plethora of regulators for each asexual developmental stage have been identified and investigated. This review summarizes our current understanding of the regulators of conidial formation, maturation, dormancy, and germination in A. nidulans.
Hypoxic-Ischemic Brain Injury in ECMO: Pathophysiology, Neuromonitoring, and Therapeutic Opportunities

Extracorporeal membrane oxygenation (ECMO), in conjunction with its life-saving benefits, carries a significant risk of acute brain injury (ABI). Hypoxic-ischemic brain injury (HIBI) is one of the most common types of ABI in ECMO patients. Various risk factors, such as history of hypertension, high day 1 lactate level, low pH, cannulation technique, large peri-cannulation PaCO2 drop (∆PaCO2), and early low pulse pressure, have been associated with the development of HIBI in ECMO patients. The pathogenic mechanisms of HIBI in ECMO are complex and multifactorial, attributing to the underlying pathology requiring initiation of ECMO and the risk of HIBI associated with ECMO itself. HIBI is likely to occur in the peri-cannulation or peri-decannulation time secondary to underlying refractory cardiopulmonary failure before or after ECMO. Current therapeutics target pathological mechanisms, cerebral hypoxia and ischemia, by employing targeted temperature management in the case of extracorporeal cardiopulmonary resuscitation (eCPR), and optimizing cerebral O2 saturations and cerebral perfusion. This review describes the pathophysiology, neuromonitoring, and therapeutic techniques to improve neurological outcomes in ECMO patients in order to prevent and minimize the morbidity of HIBI. Further studies aimed at standardizing the most relevant neuromonitoring techniques, optimizing cerebral perfusion, and minimizing the severity of HIBI once it occurs will improve long-term neurological outcomes in ECMO patients.
A Review of Recent Advances for the Detection of Biological, Chemical, and Physical Hazards in Foodstuffs Using Spectral Imaging Techniques

Traditional methods for detecting foodstuff hazards are time-consuming, inefficient, and destructive. Spectral imaging techniques have been proven to overcome these disadvantages in detecting foodstuff hazards. Compared with traditional methods, spectral imaging could also increase the throughput and frequency of detection. This study reviewed the techniques used to detect biological, chemical, and physical hazards in foodstuffs including ultraviolet, visible and near-infrared (UV-Vis-NIR) spectroscopy, terahertz (THz) spectroscopy, hyperspectral imaging, and Raman spectroscopy. The advantages and disadvantages of these techniques were discussed and compared. The latest studies regarding machine learning algorithms for detecting foodstuff hazards were also summarized. It can be found that spectral imaging techniques are useful in the detection of foodstuff hazards. Thus, this review provides updated information regarding the spectral imaging techniques that can be used by food industries and as a foundation for further studies.
A Scalable Approach to Independent Vector Analysis by Shared Subspace Separation for Multi-Subject fMRI Analysis

Joint blind source separation (JBSS) has wide applications in modeling latent structures across multiple related datasets. However, JBSS is computationally prohibitive with high-dimensional data, limiting the number of datasets that can be included in a tractable analysis. Furthermore, JBSS may not be effective if the data’s true latent dimensionality is not adequately modeled, where severe overparameterization may lead to poor separation and time performance. In this paper, we propose a scalable JBSS method by modeling and separating the “shared” subspace from the data. The shared subspace is defined as the subset of latent sources that exists across all datasets, represented by groups of sources that collectively form a low-rank structure. Our method first provides the efficient initialization of the independent vector analysis (IVA) with a multivariate Gaussian source prior (IVA-G) specifically designed to estimate the shared sources. Estimated sources are then evaluated regarding whether they are shared, upon which further JBSS is applied separately to the shared and non-shared sources. This provides an effective means to reduce the dimensionality of the problem, improving analyses with larger numbers of datasets. We apply our method to resting-state fMRI datasets, demonstrating that our method can achieve an excellent estimation performance with significantly reduced computational costs.