Miracle-dz/UniFAR-FTUs huggingface dataset:
Shenshuaifu Granule Attenuates Acute Kidney Injury by Inhibiting Ferroptosis Mediated by p53/SLC7A11/GPX4 Pathway

Background Acute kidney injury (AKI) is a common clinical condition resulting in a rapid decline in renal function, and requires improvement in effective preventive measures. Ferroptosis, a novel form of cell death, is closely related to AKI. Shenshuaifu granule (SSF) has been demonstrated to prevent AKI through suppressing inflammation and apoptosis. Objective This study aimed to explore whether SSF can inhibit ferroptosis in AKI. Methods Active ingredients in SSF were detected through HPLC-MS/MS, and their binding abilities with ferroptosis were evaluated by molecular docking. Then, male C57/BL/6J mice were randomly divided into control, cisplatin, and cisplatin+SSF groups. In the latter two groups, mice were intraperitoneally injected with 20 mg/kg of cisplatin. For five consecutive days prior to cisplatin injection, mice in the cisplatin+SSF group were gavaged with 5.2 g/kg of SSF per day. 72 h after cisplatin injection, the mice were sacrificed. Serum creatinine (SCr) and blood urea nitrogen (BUN) were measured to evaluate renal function. H&E and PAS staining were used to observe pathological damage of kidney. Cell death was observed by TUNEL staining, and iron accumulation in kidneys of mice was detected by Prussian blue staining. Western blotting, immunohistochemistry, and immunofluorescence were used to investigate the presence of inflammation, oxidative stress, mitochondrial dysfunction, iron deposition, and lipid peroxidation in mouse kidneys. Results Active ingredients in SSF had strong affinities with ferroptosis. SSF reduced SCr (p<0.01) and BUN (p<0.0001) levels, pathological damage (p<0.0001), dead cells in the tubular epithelium (p<0.0001) and iron deposition (p<0.01) in mice with cisplatin induced AKI. And SSF downregulated macrophage infiltration (p<0.01), the expressions of high mobility group box 1 (HMGB1, p<0.05) and interleukin (IL)-17 (p<0.05), upregulated superoxide dismutase (SOD) 1 and 2 (p<0.01), and catalase (CAT, p<0.05), and alleviated mitochondrial dysfunction (p<0.05). More importantly, SSF regulated iron transport and intracellular iron overload and reduced the expression of ferritin (p<0.05). Moreover, it downregulated the expressions of cyclo-oxygenase-2 (Cox-2, p<0.001), acid CoA ligase 4 (ACSL4, p<0.05), and solute carrier family 7, member 11 (SLC7A11, p<001), upregulated glutathione peroxidase 4 (GPX4, p<0.01) and p53 (p<0.01), and decreased 4-hydroxynonenal (4-HNE) level (p<0.001). Conclusion SSF attenuates AKI by inhibiting ferroptosis mediated by p53/SLC7A11/GPX4 pathway.
Deglaciation chronology of the Galloway Hills Ice Centre, southwest Scotland

During the last glacial maximum, the Galloway Hills in southwest Scotland acted as a major centre of ice dispersion within the last British–Irish Ice Sheet (BIIS). Six new or recalibrated 10Be exposure ages for samples obtained from boulders near the former ice divide yielded uncertainty‐weighted mean ages of 15.15 ± 0.72 ka (Lm scaling), or 15.33 ± 0.74 ka (Du scaling). These ages indicate that the former ice dome centred over the Galloway Hills had almost (or completely) disappeared by ca. 15 ka, imply prior deglaciation of all of southwest Scotland and refute suggestions that ice cover persisted in this area during the Lateglacial Interstade. They strongly support recent models advocating extensive deglaciation of all areas occupied by the last BIIS (apart from the Scottish Highlands) prior to warming at the onset of the Lateglacial Interstade (ca. 14.7 ka). Three samples obtained from boulders on a large latero‐terminal moraine (Tauchers moraine) near the former ice divide yielded a weighted mean age of 11.91 ± 0.77 ka (Lm scaling) or 12.01 ± 0.78 ka (Du scaling), confirming that the moraine is of Loch Lomond (Younger Dryas) Stadial age, and suggesting that the moraine was deposited 200–500 years before the end of the stade. Copyright © 2013 John Wiley & Sons, Ltd.
An Indoor Positioning Algorithm Based on Particle Filter and Neighbor-Guided Particle Optimization for Wireless Sensor Networks

In order to achieve accurate and robust indoor position estimation in a wireless sensor network using particle filtering (PF)-based positioning algorithms, particle impoverishment, a problem that aroused from the traditional replicating-and-replacing resampling operation, must be well addressed. The loss of particle diversity not only degrades positioning accuracy but can also result in filtering divergence. To address this problem, this article proposes an adaptive neighbor-guided particle optimization strategy to substitute the traditional resampling operation. The strategy optimizes the distribution of the posterior particles through three steps: neighbor radius calculation, true neighbor identification, and neighbor-guided attraction. The proposed strategy is then integrated into the PF framework to form a novel positioning algorithm referred to as adaptive neighbor-guided particle optimization-based PF algorithm (ANPOPF). The test results show that the integration of the proposed particle optimization strategy considerably enhances the robustness of the PF algorithm, mitigating the effects of particle impoverishment. With the aid of the strategy, the ANPOPF algorithm achieves higher positioning accuracy compared to several existing positioning algorithms. Moreover, the ANPOPF algorithm owns an affordable computation load for most real-time applications.
Impact of Impedance-Matching on Internal Reflections and Output Pulse Shape in Impedance-Matched Pulse Generators

Recently we developed the concept and multiple implementations of the Solid-State Impedance-Matched Marx generator (IMG). The generator combines the high-repetition rate and controllability of a solid-state (SiC MOSFET switched) Marx with the fast rise-time possibilities of the impedance-matched topology as proposed by Sandia in 2017. Proper impedance matching should maintain the rise time of the individual Marx stages at the output, regardless of the physical size of the generator. Simulations support this, and practical implementations give good results, but so far, our generators where physically smaller than the propagation time of the pulse front through the generator. When the generators become larger in comparison to pulse rise-time, improperly matched systems will show deteriorated pulse shapes, which we did not show yet. This contribution shows the effect of impedance-matching on both the generator and the load: how the physical size influences the pulse rise-time. More specifically, we take three stages of the IMG, and we connect them either with 15 mm in between, or couple them with a 600-mm coaxial transmission line, with matched and intentionally mismatched versions. This allows us to measure voltages in between the stages and increases the propagation time, which allows us to evaluate reflections on each stage. These results show that proper impedance matching makes the rise-time independent of the physical size of the generator and will lead the way for future IMGs designs.
Egg-associated Salmonella enterica serovar Enteritidis: comparative genomics unveils phylogenetic links, virulence potential, and antimicrobial resistance traits

Salmonella enterica serovar Enteritidis (SE) remains a frequent cause of foodborne illnesses associated with the consumption of contaminated hen eggs. Such a food–pathogen association has been demonstrated epidemiologically, but the molecular basis for this association has not been explored. Comparative genomic analysis was implemented to decipher the phylogenomic characteristics, antimicrobial resistance, and virulence potential of eggs-associated SE. Analyzing 1,002 genomes belonging to 841 sequence types of food-isolated SE strains suggests a high genomic similarity within the egg-related lineage, which is phylogenetically close to SE strains isolated from poultry but is different from those isolated from beef. Core genome- and single nucleotide polymorphism (SNP)-based phylogeny of 74 SE strains of egg origin showcased two distinct sublineages. Time-scaled phylogeny supported the possibility of a common ancestor of egg-related SE lineages. Additionally, genome mining revealed frequent antibiotic resistance due to the presence of aac(6’)-Iaa and mdsAB encoded on the genomes of egg-associated SE strains. For virulence gene profiling, 103–113 virulence determinants were identified in the egg-associated SE, which were comparable to 112 determinants found in human-associated SE, emphasizing the capacity of egg-associated strains to infect humans and cause diseases. The findings of this study proved the genomic similarity of egg-associated SE strains, and these were closely related to poultry strains. The egg-associated strains also harbor virulence genes equivalent to those found in human-associated SE strains. The analysis provided critical insights into the genetic structure, phylogenomics, dynamics of virulence, and antibiotic resistance of Salmonella Enteritidis, circulating in eggs and emphasizing the necessity of implementing anti-Salmonella intervention strategies, starting at the production stage of the poultry supply chain.
Prospection of Peptide Inhibitors of Thrombin from Diverse Origins Using a Machine Learning Pipeline

Thrombin is a key enzyme involved in the development and progression of many cardiovascular diseases. Direct thrombin inhibitors (DTIs), with their minimum off-target effects and immediacy of action, have greatly improved the treatment of these diseases. However, the risk of bleeding, pharmacokinetic issues, and thrombotic complications remain major concerns. In an effort to increase the effectiveness of the DTI discovery pipeline, we developed a two-stage machine learning pipeline to identify and rank peptide sequences based on their effective thrombin inhibitory potential. The positive dataset for our model consisted of thrombin inhibitor peptides and their binding affinities (KI) curated from published literature, and the negative dataset consisted of peptides with no known thrombin inhibitory or related activity. The first stage of the model identified thrombin inhibitory sequences with Matthew’s Correlation Coefficient (MCC) of 83.6%. The second stage of the model, which covers an eight-order of magnitude range in KI values, predicted the binding affinity of new sequences with a log room mean square error (RMSE) of 1.114. These models also revealed physicochemical and structural characteristics that are hidden but unique to thrombin inhibitor peptides. Using the model, we classified more than 10 million peptides from diverse sources and identified unique short peptide sequences (<15 aa) of interest, based on their predicted KI. Based on the binding energies of the interaction of the peptide with thrombin, we identified a promising set of putative DTI candidates. The prediction pipeline is available on a web server.
Volcano deformation source parameters estimated from InSAR: Sensitivities to uncertainties in seismic tomography

The eruption cycle of a volcano is controlled in part by the upward migration of magma. The characteristics of the magma flux produce a deformation signature at the Earth's surface. Inverse analyses use geodetic data to estimate strategic controlling parameters that describe the position and pressurization of a magma chamber at depth. The specific distribution of material properties controls how observed surface deformation translates to source parameter estimates. Seismic tomography models describe the spatial distributions of material properties that are necessary for accurate models of volcano deformation. This study investigates how uncertainties in seismic tomography models propagate into variations in the estimates of volcano deformation source parameters inverted from geodetic data. We conduct finite element model‐based nonlinear inverse analyses of interferometric synthetic aperture radar (InSAR) data for Okmok volcano, Alaska, as an example. We then analyze the estimated parameters and their uncertainties to characterize the magma chamber. Analyses are performed separately for models simulating a pressurized chamber embedded in a homogeneous domain as well as for a domain having a heterogeneous distribution of material properties according to seismic tomography. The estimated depth of the source is sensitive to the distribution of material properties. The estimated depths for the homogeneous and heterogeneous domains are 2666 ± 42 and 3527 ± 56 m below mean sea level, respectively (99% confidence). A Monte Carlo analysis indicates that uncertainties of the seismic tomography cannot account for this discrepancy at the 99% confidence level. Accounting for the spatial distribution of elastic properties according to seismic tomography significantly improves the fit of the deformation model predictions and significantly influences estimates for parameters that describe the location of a pressurized magma chamber.
Towards an Accelerator for Differential and Algebraic Equations Useful to Scientists

We discuss our preliminary results in building a configurable accelerator for differential equation time stepping and iterative methods for algebraic equations. Relative to prior efforts in building hardware accelerators for numerical methods, our focus is on the following: 1) Demonstrating a higher order of numerical convergence that is needed to actually support existing numerical algorithms. 2) Providing the capacity for wide vectors of variables by keeping the hardware design components as simple as possible. 3) Demonstrating configurable hardware support for a variety of numerical algorithms that form the core of scientific computation libraries. These efforts are toward the goal of making the accelerator democratically accessible by computational scientists.
The Geography of Regional Clusters in Romania and Their Importance for Entrepreneurial Activities

Regional clusters and entrepreneurship have become very popular subjects in economics, regional science, and economic geography. In the last decade, a large number of scientific papers investigate the empirical evidence for clusters, their definition, and their implications for economic policy. Also, a series of working tools for regional cluster analyses have been proposed. Entrepreneurial attitudes and activities interact and their characteristics are normally bound to the region. Local entrepreneurial attitudes and entrepreneurial activities can be the starting-point for the evolution of a regional cluster. There are lots of arguments for the hypothesis that existing regional clusters have positive impacts on entrepreneurial attitudes and entrepreneurial activities. But, exist only few analyses refering to the relationship between clusters attributes of a region and the entrepreneurial activities in the same region. From our knoledge, it is not such of analyses about Romania. This paper aims to identify regions with functional or potential industrial clusters, from Romania, and to analyse their impact on the entrepreneurial environment. Data about all the companies acting in Romania in 2011 is used to elaborate the spatial clusters in the most concentrated Romania industries. A second data set, collected as part of the Global Entrepreneurship Monitor (GEM), serves to assess the relationship between regional clusters and entrepreneurial activities and attitudes. GEM intends to measure the level of entrepreneurial activities and to identify the individual factors and the entrepreneurial framework conditions that may explain differences in the levels between the regions of Romania and the entire country.The paper tests the empirically proven hypothesis which stipulates that the existence of one or several industrial clusters in a region has a positive impact on the number of start-ups and attitudes in the same region. The results obtained from descriptive and regression analyses have shown that there is a positive relationship between the number of clusters and the number of employees in clusters in Romanian regions on the one hand and entrepreneurial activities and entrepreneurial attitudes like the assessment of good start-up opportunities on the other.
Comparing various dyeing techniques using plant extracts on cotton fabrics

Natural dyes were extracted from marigold flowers, onion outer skins, fenugreek seeds, and green coffee, and were used to color 100% Giza 86 cotton fabric using alum as a mordant. The dyeing process was conducted using three techniques: conventional, microwave, and infrared radiation. The surface properties of the cotton fabric were evaluated using FTIR spectroscopy. Additionally, the dyed fabrics were assessed for color strength (K/S) and fastness properties including washing, light, rubbing, and perspiration fastness. The results indicated a moderate affinity between cotton fabric and the extracted dye. The use of metal mordant improved dye exhaustion in alum-dyed cotton fabrics. There was a significant variation in color shade depth among the different natural dyes. Specifically, marigold extract resulted in a significantly darker yellow shade, while green coffee extract yielded a lighter yellow hue. The dye derived from marigold flowers demonstrated exceptional efficiency in color fastness tests, with the microwave dyeing process yielding the best results for (K/S). However, the use of green coffee and fenugreek in dyeing resulted in superior fastness compared to other methods.
MECHANISTIC CALCULATION OF LOAD EQUIVALENCY AND TRUCK FACTORS FOR FATIGUE DAMAGE OF HOT MIX ASPHALT USING STRAIN AREA METHOD

Among several pavement distress types, fatigue and rutting are the main distresses of flexible pavements. To accurately assess the amount of damage that trucks with multiple axle groups cause to these flexible pavements, a summation methodology is imperative. Several methods have been used to sum the pavement damage due to multiple axle groups, researchers have used continuous methods, as well as discrete methods. The continuous strain area method is a very good candidate for calculating the fatigue damage. Applying this method on the laboratory strain pulses proved superior to the discrete method. The characteristics of the mechanistic strain pulse differs from the laboratory strain pulse, which indicates that the mechanistic strain area method for calculating the Axle Factors (AFs) needs to be calibrated with the laboratory-derived AF values. The calibrated power was obtained by minimizing the Sum of the Square Error (SSE) between both AFs that involved iteration over trial values. This study employed two pavement cross sections, thin and thick, for analysis of the mechanistic strain area. The Load Equivalency Factors (LEFs) and Truck Factors (TFs) for multiple axle and truck configurations were calculated using the calibrated mechanistic strain area method. The results showed that combining truck axles in a large axle group reduces the fatigue damage significantly (by about 50%) compared to the same number of individual axles. Moreover, wide-base tires impose more fatigue damage to the pavements than conventional dual tires bearing the same load and tire pressure.
Education Case on Deep Learning Based Electric Machine Automatic Fault Detection

Automatic fault detection represents a key asset in the predictive maintenance field. Unfortunately, the application of techniques based on Machine Learning (ML) is not widespread in traditional educational curricula. This paper presents a technical lecture to familiarize graduate students and predictive maintenance practitioners with Deep Learning (DL) tools for automatic fault detection. Vibration data from an open source bearing dataset is utilized for training and testing a Convolutional Neural Network (CNN) model. The presented content is developed in a Matlab interface, which is a widespread tool across academic institutions worldwide.
Understanding the role attitudes could play in conservation planning for wolves and brown bears in Abruzzo, Lazio and Molise National Park, Italy

Wolf and brown bear populations are expanding throughout Europe, in a human dominated landscape. Conservation of these two species will be determined by the attitudes of those who live close to them. Unlike in North America, human dimensions (HD) regarding human-wildlife issues remains a relatively new field of research in Europe, and even more so in Italy. This is the first study of HD in wolf and bear management in Italy. -- This dissertation has focused on understanding how the attitudes of those living in close proximity to both wolves and bears can play a role in achieving conservation planning. -- Attitudes are positive of negative evaluations of an object - in this case wolves or bears - and are a mental state composed by affective (feelings), cognitive (beliefs) and behavioural intention components. Each component of attitude plays a role in the conservation of wolves and brown bears. The objectives of this study were to look in detail at these three components, how they can be linked, and how they contribute to conservation. Quantitative face-to-face (n=1611) interviews were carried out to determine attitudes of residents toward wolves and bears in the Abruzzo, Lazio, and Molise National Park (PNALM) and the surrounding buffer zone. -- This dissertation demonstrated that the majority of residents in the PNALM are willing to coexist with these large carnivores. Participants expressed positive feelings toward wolves and bears, they tolerated the perceived damages caused, and they support the maintenance and protection of both species - but especially of brown bears. This dissertation showed that residents have a higher level of knowledge about bears, which results in stronger positive feelings. -- These are important messages to communicate to managers responsible for the conservation of wolves and brown bears. Emphasizing these positive findings can be the staring point for constructive dialogue on conservation. This study, therefore, sets the direction for the future public involvement processes. The next HD step would be to organize workshops with all interest groups (e.g. shepherds, hunters, non-locals), to bring them together and to work with them on their commonalities to create a management plan for wolves and bears.
Another Chance: Adaptive reuse of the built heritage strategies for circular creativity

The study delves into the realm of adaptive reuse, exploring its potential in sustainable urban development, particularly focusing on public buildings within the cultural and creative sectors. Through a multiple case study analysis, both qualitative and quantitative methods were employed to scrutinize design strategies and spatial transformations in recent adaptive reuse projects of existing structures. This research investigates the evolution of adaptive reuse, highlighting its historical and theoretical underpinnings, and subsequently examines contemporary approaches towards existing structures in cultural, creative, and public domains. The study findings reveal common characteristics and innovative design strategies employed in recent adaptive reuse projects, emphasizing the transformative potential of neglected or abandoned urban spaces. Utilizing a comprehensive methodology involving case study analyses and diverse data collection techniques, the research underscores the significance of adaptive reuse as an established practice in contemporary architectural and urban design. The article's contribution to the social and economic dimensions of urban development lies in understanding and promoting sustainable, resource-saving strategies. This work paves the way for future research, suggesting potential expansions in creating an 'atlas of adaptive reuse' and exploring comparative analyses between existing reuse and new construction, specifically focusing on public buildings with civic-cultural uses.