Miracle-dz/UniFAR-FTUs huggingface dataset:
Reorganization and Suppression of Store-Operated Calcium Entry in Podocytes of Type 2 Diabetic Rats

Type 2 diabetes mellitus (DM2) is a widespread metabolic disorder that results in podocyte damage and diabetic nephropathy. Previous studies demonstrated that TRPC6 channels play a pivotal role in podocyte function and their dysregulation is associated with development of different kidney diseases including nephropathy. Here, using single channel patch clamp technique, we demonstrated that non-selective cationic TRPC6 channels are sensitive to the Ca2+ store depletion in human podocyte cell line Ab8/13 and in freshly isolated rat glomerular podocytes. Ca2+ imaging indicated the involvement of ORAI and sodium–calcium exchanger in Ca2+ entry induced upon store depletion. In male rats fed a high-fat diet combined with a low-dose streptozotocin injection, which leads to DM2 development, we observed the reduction of a store-operated Ca2+ entry (SOCE) in rat glomerular podocytes. This was accompanied by a reorganization of store-operated Ca2+ influx such that TRPC6 channels lost their sensitivity to Ca2+ store depletion and ORAI-mediated Ca2+ entry was suppressed in TRPC6-independent manner. Altogether our data provide new insights into the mechanism of SOCE organization in podocytes in the norm and in pathology, which should be taken into account when developing pharmacological treatment of the early stages of diabetic nephropathy.
The Planetary and Lunar Ephemeris DE 421

The planetary and lunar ephemeris DE 421 represents updated estimates of the orbits of the Moon and planets. The lunar orbit is known to submeter accuracy through fitting lunar laser ranging data. The orbits of Venus, Earth, and Mars are known to subkilometer accuracy. Because of perturbations of the orbit of Mars by asteroids, frequent updates are needed to maintain the current accuracy into the future decade. Mercury’s orbit is determined to an accuracy of several kilometers by radar ranging. The orbits of Jupiter and Saturn are determined to accuracies of tens of kilometers as a result of spacecraft tracking and modern ground-based astrometry. The orbits of Uranus, Neptune, and Pluto are not as well determined. Reprocessing of historical observations is expected to lead to improvements in their orbits in the next several years.
Toward Objective, Standardized Intensity Estimates from Surface Wind Speed Observations

Extreme wind climatology and event-specific intensity assessments rely heavily on surface wind field observations. The most widely used platforms sited at airports are the Automated Surface Observing System (ASOS) and its predecessor, the Automated Weather Observing System (AWOS). The terrain immediately surrounding most of these stations may be nominally characterized as aero-dynamically very smooth because of the runways and flat expanses of grass that define most airport layouts. Outside of most airports, a wide spectrum of marine, open, suburban, and heavily built-up terrain conditions are present. The results of this research indicate that the wind speeds recorded by AWOS/ASOS are deeply sensitive to this terrain. Prior research has shown that direct usage of the raw surface data can introduce surface-layer wind speed errors on the order of 30%–40% due to terrain effects. Similar values are observed for gust speeds in this paper, when averaging technique and anemometer response characteristics are co.
The Role of Future Parenthood in Hiring: Can Motherhood Penalties and Fatherhood Bonuses be Generalized to Future Mom and Dad?

Research has shown motherhood penalties and fatherhood bonuses in working places. Employers, it appears, view parenthood as an important factor in hiring. However, motherhood penalties and fatherhood bonuses could be generalized to future parents, since during Chinese hiring process, child-free job applicants are often asked about their future parenthood plans. In this research proposal, an experimental study in a lab setting is utilized to test whether the role of future parenthood could be sufficient to cause hiring bias. The main set of independent variables in the study is applicants different expectation of future parenthood. Applicants gender and applicants applying organization type are also included to see whether there are some interaction effects with future parenthood. The results are measured through participants explicit and implicit attitudes towards fictitious resumes using 3 measurements: passing rate, a 7-point scale, and decision making time. The predicted results are that there will be a significant main effect of future parenthood, a significant interaction effect of parenthood and gender, and a significant interaction effect of parenthood and organization type in the hiring process.
Machine learning enhancement of a digital twin for wavelength division multiplexing network performance prediction leveraging quality of transmission parameter refinement

Digital twins capable of quality of transmission (QoT) estimation and prediction are powerful tools for efficient design and operation of optical networks. In this paper, we employ machine learning techniques to enhance both the QoT estimation and prediction capabilities of an optical network digital twin. By leveraging a method to infer or refine the unknown lumped loss distributions and amplifier gain spectra for accurate characterization of the current optical network state, the accuracy of estimation and prediction is substantially improved. For QoT prediction, we further develop a novel neural-network architecture for erbium-doped fiber amplifiers that, after factory training on a single device and with fully loaded configurations only, generalizes to partially loaded configurations seen after deployment and to different gain and tilt settings and other physical devices of the same type. We combine refined parameters and a novel method that predicts the difference in per-channel power when individual services are added or removed or multiple services are lost due to a fiber cut. The impact of error amplification due to cascading of individual components’ models is shown to be reduced, yielding predictions that are substantially more accurate than simply cascaded predictions. As the network ages, the digital twin can be updated by retraining while using only information available in the current network state.
Mesh generation and fracture for deformable bodies

Numerical simulation complements both theory and experimentation as an important component of today's research. This thesis focuses on the tightly-woven relationship between the simulation of a Lagrangian solid and its discretization. We introduce a mesh generation technique as well as a new method for simulating meshes and materials with changing topology. The tetrahedral mesh generation algorithm is designed for the Lagrangian simulation of deformable bodies. The algorithm's input is a level set. First a bounding box of the object is covered with a uniform lattice of subdivision-invariant tetrahedra. The level set is then used to guide a red green adaptive subdivision procedure that is based on both the local curvature and the proximity to the object boundary. The final topology is carefully chosen so that the connectivity is suitable for large deformation and the mesh approximates the desired shape. Finally, this candidate mesh is compressed to match the object boundary. To maintain element quality during this compression phase we relax the positions of the nodes using finite elements, masses and springs, or an optimization procedure. We also propose a virtual node algorithm that allows material to separate along arbitrary paths through a mesh. The material within an element is fragmented by creating several replicas of the element and assigning a portion of real material to each replica. This results in elements that contain both some real material and empty regions. Our new virtual node algorithm automatically determines the number of replicas and the assignment of material to each. Moreover, it provides the degrees of freedom required to simulate the partially or fully fragmented material. This approach provides for the efficient simulation of complex geometry with a simple mesh, i.e. the geometry need not align itself with element boundaries. It also alleviates many shortcomings of traditional Lagrangian simulation techniques for meshes with changing topology. For example, slivers do not require small time step restrictions since they are embedded in well shaped larger elements.
Chemical Constituents from the Roots of Angelica reflexa That Improve Glucose-Stimulated Insulin Secretion by Regulating Pancreatic β-Cell Metabolism

The aim of this study was to discover bioactive constituents of Angelica reflexa that improve glucose-stimulated insulin secretion (GSIS) in pancreatic β-cells. Herein, three new compounds, namely, koseonolin A (1), koseonolin B (2), and isohydroxylomatin (3), along with 28 compounds (4–31) were isolated from the roots of A. reflexa by chromatographic methods. The chemical structures of new compounds (1–3) were elucidated through spectroscopic/spectrometric methods such as NMR and HRESIMS. In particular, the absolute configuration of the new compounds (1 and 3) was performed by electronic circular dichroism (ECD) studies. The effects of the root extract of A. reflexa (KH2E) and isolated compounds (1–31) on GSIS were detected by GSIS assay, ADP/ATP ratio assay, and Western blot assay. We observed that KH2E enhanced GSIS. Among the compounds 1–31, isohydroxylomatin (3), (−)-marmesin (17), and marmesinin (19) increased GSIS. In particular, marmesinin (19) was the most effective; this effect was superior to treatment with gliclazide. GSI values were: 13.21 ± 0.12 and 7.02 ± 0.32 for marmesinin (19) and gliclazide at a same concentration of 10 μM, respectively. Gliclazide is often performed in patients with type 2 diabetes (T2D). KH2E and marmesinin (19) enhanced the protein expressions associated with pancreatic β-cell metabolism such as peroxisome proliferator-activated receptor γ, pancreatic and duodenal homeobox 1, and insulin receptor substrate-2. The effect of marmesinin (19) on GSIS was improved by an L-type Ca2+ channel agonist and K+ channel blocker and was inhibited by an L-type Ca2+ channel blocker and K+ channel activator. Marmesinin (19) may improve hyperglycemia by enhancing GSIS in pancreatic β-cells. Thus, marmesinin (19) may have potential use in developing novel anti-T2D therapy. These findings promote the potential application of marmesinin (19) toward the management of hyperglycemia in T2D.
Some Extension of Sparse Principal Component Analysis

Given a covariance matrix, sparse principal component analysis (SPCA) considers the problem of maximizing the variance explained by a particular linear combination of the input variables where the number of nonzero coefficients is constrained. In some applications, the coefficients in this combination are required to be non-negative. Moreover, when loading an input variable is associated an individual cost, we need incorporate weights, which represent the loading cost of input variables, into sparsity constraint. And in this paper, we consider problems of SPCA with weighted sparsity constraint and/or non-negative sparsity constraint. These problems are reduced to solving some semi-definite programming ones via convex relaxation technique. Numerical results show that the method is efficient and reliable in practice.
Factors Influencing Consumers’ Organic Food Continuous Purchase Intentions during the Post-Pandemic Era: An Empirical Investigation in China

During the evolutionary phases of the COVID-19 pandemic, consumers’ eating habits and spending behaviours have progressively shifted to the pursuit of the safer and healthier food products, such as organic food. Therefore, this study investigated the factors affecting Chinese buyers’ organic food continuous purchase intentions (CPI) during the post-pandemic era. To better adapt to the current consumption context in China, this study proposed a modified TPB framework (M-TPB), by replacing subjective norms with Chinese cultural variables, such as face consciousness and group conformity, and adding constructs of perceived value of organic food (PVOF), health consciousness, and the impact of COVID-19 (IOC). Convincingly, experimental results from a structural equation model analysis of 460 usable responses indicate that M-TPB has superior explanatory power (R2 = 65%) compared with the TPB model (R2 = 40%) for explaining organic food CPI during the post-pandemic period. The path analysis demonstrated that perceived behavioural control, attitude, face consciousness, group conformity, health consciousness, IOC, and PVOF had substantial positive effects on CPI, while subjective norms were not significantly related. Moreover, IOC exhibited a positive and significant relationship with health consciousness and PVOF. These findings can be useful for stakeholders in the Chinese organic food industry to generate timely promoting strategies during the post-pandemic period.
What is the Consumption-CAPM Missing? An Information-Theoretic Framework for the Analysis of Asset Pricing Models

We study a broad class of asset pricing models in which the stochastic discount factor (SDF) can be factorized into an observable component and a potentially unobservable, model-speci…c, one. Exploiting this decomposition we derive new entropy bounds that restrict the admissible regions for the SDF and its components. Without using this decomposition, to a second order approximation, entropy bounds are equivalent to the canonical Hansen-Jagannathan bounds. However, bounds based on our decomposition have higher information content, are tighter, and exploit the restriction that the SDF is a positive random variable. Our information-theoretic framework also enables us to extract a non-parametric estimate of the unobservable component of the SDF. Empirically, we …nd it to have a business cycle pattern, and signi…cant correlations with both …nancial market crashes unrelated to economy-wide contractions, and the Fama-French factors. We apply our methodology to some leading consumption-based models, gaining new insights about their empirical performance.
Magnetic Properties and Magnetocaloric Effect of Polycrystalline and Nano-Manganites Pr0.65Sr(0.35−x)CaxMnO3 (x ≤ 0.3)

Here we report investigations of bulk and nano-sized Pr0.65Sr(0.35−x)CaxMnO3 compounds (x ≤ 0.3). Solid-state reaction was implemented for polycrystalline compounds and a modified sol–gel method was used for nanocrystalline compounds. X-ray diffraction disclosed diminishing cell volume with increasing Ca substitution in Pbnm space group for all samples. Optical microscopy was used for bulk surface morphology and transmission electron microscopy was utilized for nano-sized samples. Iodometric titration showed oxygen deficiency for bulk compounds and oxygen excess for nano-sized particles. Measurements of resistivity of bulk samples revealed features at temperatures associated with grain boundary condition and with ferromagnetic (FM)/paramagnetic (PM) transition. All samples exhibited negative magnetoresistivity. Magnetic critical behavior analysis suggested the polycrystalline samples are governed by a tricritical mean field model while nanocrystalline samples are governed by a mean field model. Curie temperatures values lower with increasing Ca substitution from 295 K for the parent compound to 201 K for x = 0.2. Bulk compounds exhibit high entropy change, with the highest value of 9.21 J/kgK for x = 0.2. Magnetocaloric effect and the possibility of tuning the Curie temperature by Ca substitution of Sr make the investigated bulk polycrystalline compounds promising for application in magnetic refrigeration. Nano-sized samples possess wider effective entropy change temperature (ΔTfwhm) and lower entropy changes of around 4 J/kgK which, however, puts in doubt their straightforward potential for applications as magnetocaloric materials.
Exploring m6A‐RNA methylation as a potential therapeutic strategy for acute lung injury and acute respiratory distress syndrome

Abstract N6‐methyladenosine (m6A) is the most common methylation modification in mammalian messenger RNA (mRNA) and noncoding RNAs. m6A modification plays a role in the regulation of gene expression and deregulation of m6A methylation has been implicated in many human diseases. Recent publications suggest that exploitation of this methylation process may possess utility against acute lung injury (ALI). ALI and its more severe form, acute respiratory distress syndrome (ARDS) are acute, inflammatory clinical syndromes characterized by poor oxygenation and diffuse pulmonary infiltrates. This syndrome is associated with microvascular endothelial dysfunction, subsequent pulmonary hypertension and may ultimately lead to mortality without rigorous and acute clinical intervention. Over the years, many attempts have been made to detect novel therapeutic avenues for research without much success. The urgency for the discovery of novel therapeutic agents has become more pronounced recently given the current pandemic infection of coronavirus disease 2019 (COVID‐2019), still ongoing at the time that this review is being written. We review the current landscape of literature regarding ALI and ARDS etiology, pathophysiology, and therapeutics and present a potential role of m6A methylation. Additionally, we will establish the axiomatic principles of m6A methylation to provide a framework. In conclusion, METTL3, or methyltransferase‐like 3, the selective RNA methyltransferase for m6A, is a hub of proinflammatory gene expression regulation in ALI, and using a modern drug discovery strategy will identify new and effective ALI drug candidates targeting METTTL3.
Asset Pricing and Risk Sharing Implications of Alternative Pension Plan Systems

We show that incorporating defined benefit pension funds in an asset pricing model with incomplete markets improves its ability to jointly match the historical equity premium and riskless rate, and has important implications for risk sharing. We emphasize the importance of the pension fund’s size and asset demands in determining equilibrium asset prices and discuss a new risk channel arising from fluctuations in the fund’s endowment. We use our calibrated model to study the implications of a shift from an economy with defined benefit pension schemes to one with defined contribution plans. We find that the new steady-state is characterized by a higher riskless rate and a lower equity premium. Consumption volatility increases for retirees but decreases for workers. JEL Classification: E21, E44, G11, G12, G50.
The Effect of Problem-based Learning through Blended Learning on Digital Literacy of Eleventh-Grade Students on Excretory System Material

This study aims to analyze students’ digital literacy through problem-based Learning (PBL) through Blended Learning (BL). The research was carried out in the eleventh grade of SMA Negeri Plus Riau in the even semester of the 2021/2022 academic year. This research is quasi-experimental, using a closed questionnaire with 4 Linkert scales. The research parameter is students’ digital literacy with eight indicators: Functional Skills and Beyond, Creativity, Collaboration, Communication, The Ability to Find and Select Information, Critical Thinking and Evaluation, Cultural and Social Understanding, and E-Safety. Data were obtained using the Linkert scale and converted into Value (N). Value every aspect is the score obtained from the answer indicators that are checked (considered the most appropriate) and divided by the maximum score multiplied by 100. The average value of each aspect is obtained from the total value of each indicator divided by the number of indicators, then analyzed descriptively and qualitatively. Digital literacy students in the control class got an average score of 77.5 (good category) and 85.25 in the experimental class (very good category). Of the eight indicators, the highest digital literacy indicator in the control and experimental classes is creativity, with scores of 85 and 88, respectively. The E-Safety indicator scores 85 and 90. The lowest score was the Critical Thinking and Evaluation indicator, in the control and experimental classes, with a 75, categorized as good. Problem-based learning model through blended learning positively affects the digital literacy of eleventh-grade students of SMA Negeri Plus Riau on the excretory system material.
Biodiversity-Based Empirical Critical Loads of Nitrogen Deposition in the Athabasca Oil Sands Region

Anthropogenic nitrogen (N) emissions can have considerable effects on terrestrial ecosystems, with chronic N deposition leading to changes in plant species composition. The Athabasca Oil Sands Region (AOSR) represents a large point source of N emissions, which has prompted concern for surrounding habitats. The objective of this study was to determine the relative importance of N deposition as a driver of plant species community composition against bioclimatic and soil chemical variables. Further, we sought to identify community thresholds in plant species composition across a N deposition gradient. This assessment was performed for 46 Jack pine (Pinus banksiana Lamb.)-dominant forest sites surrounding the AOSR spanning Alberta and Saskatchewan. In total, 35 environmental variables were evaluated using redundancy analysis (RDA), followed by gradient forest analysis applied to plant species abundance data. Soil chemical variables accounted for just over 26% of the total explainable variation in the dataset, followed by bioclimatic variables (19%) and deposition variables (5%), but joint effects between variables also explained a significant portion of the total variation (p < 0.001). Total deposited nitrogen (TDN), and sulphur (TDS) along with bioclimatic and soil chemical variables, were identified as important variables in gradient forest analysis. A single, definitive threshold across TDN was identified at approximately 5.6 kg N ha−1 yr−1 (while a TDS threshold was found at 14.4 kg S ha−1 yr−1). The TDN threshold range was associated primarily with changepoints for several vascular species (Pyrola asarifolia, Pyrola chlorantha, Cornus canadensis, and Arctostaphylos uva-ursi) and bryophyte and lichen species (Pleurozium schreberi, Vulpicida pinastri, and Dicranum polysetum). These results suggest that across Jack pine-dominant forests surrounding the AOSR, the biodiversity-based empirical critical load of nutrient N is 5.6 kg N ha−1 yr−1.
Estimating Individual Tree Mortality in the Sierra Nevada Using Lidar and Multispectral Reflectance Data

Widespread tree mortality events occur during periods of severe drought in temperate conifer forests and are expected to become more frequent in many areas due to climate change. Improved mapping of individual tree mortality is needed to identify risk factors and design effective conservation strategies. In this study, we used National Ecological Observatory Network (NEON) lidar and multispectral reflectance airborne observations to map individual tree mortality over a 160 km2 area during and after the 2012–2016 drought for two sites in California's Sierra National Forest. We used NEON lidar to derive tree locations and crown perimeters and multispectral data to map tree mortality for more than 1 million trees. We found that 25.4% of the trees in our study area died between 2013 and 2017, with considerably higher mortality at the lower‐elevation Soaproot Saddle site. Between 2017 and 2019, an additional 2.0%–2.8% of the trees died each year. Two wildfires in 2020 and 2021 increased tree mortality within burned area perimeters by 49%–89% between 2019 and 2021. Consistent with previous work, we found that tree mortality risk increased as a function of tree height. Tree mortality was positively associated with distance from rivers, trees per hectare, and decreasing slope at the lower elevation site. In contrast, increasing slope was positively associated with tree mortality at the higher elevation site. Our approach and dataset provide a means to study the combined effects of drought and wildfire on tree mortality and may improve projections of forest resilience under a changing climate.