SE Model Research Articles

Spectroscopic ellipsometry investigation of a 6H–SiC single crystal plate for potential use in graphene optoelectronic devices

At room temperature, the spectroscopic ellipsometry SE parameters Epsi (ψ) and Delta (Δ) for 6H–SiC crystal substrate were measured in the wavelength range 350–1700 nm at four different angles of incidence, 60°, 65°, 70°, and 75°. An SE model was developed to extract optical consents of 6H–SiC crystal substrate from the experimentally measured SE parameters. In the wavelength range 350–1700 nm, the refractive index of the 6H–SiC crystal substrate was extracted and fitted to two terms of the Sellmeier dispersion function. In the wide spectral range (350–1700 nm), the Wemple-DiDomenico (WDD) dispersion model shows how the real refractive index changes with wavelength. The analysis of refractive index dispersion of the 6H–SiC crystal substrate using Sellmeier dispersion function and WDD optical model allows to extract oscillator strength Nfij, average oscillator energy Eo, dispersion energy of the single oscillator Ed, lattice oscillator strength El, Abbe dispersion number, energy gap Eg, density of valence electrons nv, refractive index at infinite wavelength n∞, lattice dielectric constant εL, the ratio of free carrier density to free carrier effective mass (N/m∗), and plasma frequency ωp. For the 6H–SiC crystal substrate, in the desired spectral range (240–1700 nm), it does not have any depolarization effect on the reflected polarized light.

A detailed practical and theoretical study of the adsorptive potential of synthesised ceria impregnated activated graphite for aqueous Se(IV) and Te(IV) removal: Process optimisation, characterisation and mechanistic approach

A cost-effective, simple, and earth friendly nanocomposite adsorbent was synthesized by in - situ penetration of ceria nanocrystals on graphite to remove aqueous selenium (IV) and tellurium (IV). Batch experiments optimize parameters such as pH, temperature, sorbent dosage and equilibration time of 30 minutes. The adsorbent exhibited the maximum adsorption capacities of 144.5 mg/g for Te(IV) and 29.3 mg/g for Se(IV). BET analysis revealed a specific surface area of 16.44 m²/g, an average pore diameter of 4.24 nm, and a high pore volume of 0.0519 cm³/g. The adsorbent was characterized before and after adsorption by SEM-EDX, XRD and FTIR. The sorption data could be well interpreted by the Langmuir model for both Se and Te and by the DR isotherm for Te(IV) at ambient temperature. Kinetic empirical data correlated positively with pseudosecond order for both Se and Te and the Elovich model for Te at room temperature. Kinetics and equilibrium isotherms suggested that the adsorption occurred through surface adsorption using electrostatic bonding along with physisorption involving steady diffusion and accommodation of analyte ions into mesopores and defects of the adsorbent. Chemisorption by electron transfer of the oxyanions of Se/Te with Ce3+/Ce4+ of the cerium impregnated graphite (CIAG) and ion exchange with functional groups on the activated graphite edges were other probable mechanisms. The adsorbent demonstrated the ability to be regenerated and reused over multiple cycles, with minimal interference from coexisting ions emphasizing its selectivity. All measurements were conducted in triplicate with the relative standard deviation (RSD) below 5 %. The optimised method was successfully applied in natural water samples from various sources, demonstrating strong potential for scaling up to field applications.

Potential neuroprotective and autophagy-enhancing effects of alogliptin on lithium/pilocarpine-induced seizures in rats: Targeting the AMPK/SIRT1/Nrf2 axis

BackgroundStatus epilepticus (SE) as a severe neurodegenerative disease, greatly negatively affects people's health, and there is an urgent need for innovative treatments. The valuable neuroprotective effects of glucagon-like peptide-1 (GLP-1) in several neurodegenerative diseases have raised motivation to investigate the dipeptidyl peptidase-4 (DPP-4) inhibitor; alogliptin (ALO), an oral antidiabetic drug as a potential treatment for SE. ALO has shown promising neuroprotective effects in Alzheimer's and Parkinson's diseases, but its impact on SE has not yet been studied. AimThe present study aimed to explore the repurposing potential for ALO in a lithium/pilocarpine (Li/Pil)-induced SE model in rats. Main methodsALO (30 mg/kg/day) was administered via gavage for 14 days, and SE was subsequently induced in the rats using a single dose of Li/Pil (127/60 mg/kg), while levetiracetam was used as a standard antiepileptic drug. Key findingsThe results showed that ALO reduced seizure severity and associated hippocampal neurodegeneration. ALO also increased γ-aminobutyric acid (GABA) levels, diminished glutamate spikes, and corrected glial fibrillary acidic protein (GFAP) changes. At the molecular level, ALO increased GLP-1 levels and activated its downstream signaling pathway, AMP-activated protein kinase (AMPK)/sirtuin-1 (SIRT1). ALO also dampened the brain's pro-oxidant response, curbed neuroinflammation, and counteracted hippocampal apoptosis affording neuroprotection. In addition, it activated autophagy as indicated by Beclin1 elevation. SignificanceThis study suggested that the neuroprotective properties and autophagy-enhancing effects of ALO make it a promising treatment for SE and can potentially be used as a management approach for this condition.

Neuroprotective potential of topiramate, pregabalin and lacosamide combination in a rat model of acute SE and intractable epilepsy: Perspectives from electroencephalographic, neurobehavioral and regional degenerative analysis

The lithium-pilocarpine model is commonly used to recapitulate characteristics of human intractable focal epilepsy. In the current study, we explored the impact of topiramate (TPM) alone and in combination with pregabalin and lacosamide administration for 6 weeks on the evolution of spontaneous recurrent seizures (SRS) and disease-modifying potential on associated neuropsychiatric comorbidities. In addition, redox impairments and neurodegeneration in hippocampus regions vulnerable to temporal lobe epilepsy (TLE) were assessed by cresyl violet staining. Results revealed that acute electrophysiological (EEG) profiling of the ASD cocktail markedly halted sharp ictogenic spikes as well as altered dynamics of brain wave oscillations thus validating the need for polytherapy vs. monotherapy. In TLE animals, pharmacological intervention for 6 weeks with topiramate 10 mg/kg in combination with PREG and LAC at the dose of 20 mg/kg exhibited marked protection from SRS incidence, improved body weight, offensive aggression, anxiety-like behavior, cognitive impairments, and depressive-like behavior (p < 0.05). Moreover, combination therapy impeded redox impairments as evidenced by decreased MDA and AchE levels and increased activity of antioxidant SOD, GSH enzymes. Furthermore, polytherapy rescued animals from SE-induced neurodegeneration with increased neuronal density in CA1, CA3c, CA3ab, hilus, and granular cell layer (GCL) of the dentate gyrus. In conclusion, early polytherapy with topiramate in combination with pregabalin and lacosamide prompted synergy and prevented epileptogenesis with associated psychological and neuropathologic alterations.

Pharmacological elevation of glutathione inhibits status epilepticus-induced neuroinflammation and oxidative injury

Glutathione (GSH) is a major endogenous antioxidant, and its depletion has been observed in several brain diseases including epilepsy. Previous studies in our laboratory have shown that dimercaprol (DMP) can elevate GSH via post-translational activation of glutamate cysteine ligase (GCL), the rate limiting GSH biosynthetic enzyme and inhibit neuroinflammation in vitro. Here we determined 1) the role of cysteamine as a new mechanism by which DMP increases GSH biosynthesis and 2) its ability to inhibit neuroinflammation and neuronal injury in the rat kainate model of epilepsy. DMP depleted cysteamine in a time- and concentration-dependent manner in a cell free system. To guide the in vivo administration of DMP, its pharmacokinetic profile was determined in the plasma, liver, and brain. The results confirmed DMP's ability to cross the blood-brain-barrier. Treatment of rats with DMP (30 mg/kg) depleted cysteamine in the liver and hippocampus that was associated with increased GCL activity in these tissues. GSH levels were significantly increased (20 %) in the hippocampus 1 h after 30 mg/kg DMP administration. Following DMP (30 mg/kg) administration once daily, a marked attenuation of GSH depletion was seen in the SE model. SE-induced inflammatory markers including cytokine release, microglial activation, and neuronal death were significantly attenuated in the hippocampus with DMP treatment. Taken together, these results highlight the importance of restoring redox status with rescue of GSH depletion by DMP in post epileptogenic insults.

Effects and recommendations of crack tip modelling on compliance solutions

Elastic unloading compliance is a common technique to determine instantaneous crack size in fracture mechanics properties such as resistance curves (R-curves) and fatigue crack growth. This technique uses a polynomial, developed using finite elements models, to correlate compliance with crack depth. In these models, crack tip is usually modeled with a blunt mesh (with a crack tip radius ρ) or a sharp mesh (with element size in the width direction l), and neither are standardized in the literature. This paper shows that both affect compliance, therefore potentially impacting results of fracture mechanics properties acquired from simulations. This paper focuses on how those parameters affects compliance in SE(B) models. Finally, a parameter φ that normalizes ρ or l with the width was developed and recommended values based on compliance convergence are suggested. Controlling φ may help to ensure reliable results in fracture mechanics models where compliance needs to be determined.

Preservice Teachers’ Assessment Literacy Within Models-Based Practice

Purpose: Using the assessment literacy framework, the purpose of this study was to explore preservice teachers understanding and enactment of the message system during an early field experience. Method: Six PTs employed SE seasons totaling 540 min. Seven qualitative data methods were utilized. Data analysis included deductive and inductive analysis using a thematic approach. Trustworthiness included data triangulation, peer debriefing, negative case analysis, and the maintenance of an audit trail. Results: Three main themes derived from the data included: instructional decisions were driven by the SE model and informal assessment; formal assessment was driven by the structure of SE; and high assessment value but low assessment literacy. Discussion/Conclusion: The SE model drove PTs to use formal assessment, but primarily in an evaluative manner. The PTs lacked assessment literacy, felt pressure to follow the model, did not use assessment to inform teaching, and had concerns for future assessment use.

Mapping student engagement in health professions education policy and decision-making: a scoping review

Background“Student engagement” (SE) is gaining momentum as an approach to improve the performance of health professions education (HPE). Nevertheless, despite the broad studies about the role of students in various areas, little is known about the role of SE in policy and decision-making activities. This study aimed to map SE in policy and decision-making regarding terms and definitions, engagement models, influencing factors, outcomes and achievements, and the interconnection between the influencing factors.MethodFive databases (PubMed, Scopus, ProQuest, Web of Science, and ERIC) were systematically searched from Jan 1, 1990, to Nov 12, 2022. The review was followed according to the Arksey and O’Malley framework for scoping reviews and reported according to the PRISMA-ScR guidelines. We included articles published in English focusing on HPE policy and decision-making. The authors summarized and synthesized the findings into themes, subthemes, tables, and models.ResultsOf the 22 articles included in the full-text review, terms and definitions were tabled, and three themes were extracted: 1. models of SE, in which 10 studies (45.5%) presented the highly structured formal models as Organizations, 5 studies (22.7%) reported less-structured community and group as Programs, and 7 studies (31.8%) engaged students only in surveys or interviews as Perspective; 2. Factors influencing SE, that were categorized into 7 subthemes: structural, environmental, and motivational factors, member characteristics, training and mentoring, member relationships, valuing and recognizing. 3. Outcomes and achievements of SE related to systems and members. The interconnection between influencing factors is also demonstrated as a conceptual model.DiscussionThere are various SE models in HPE policy and decision-making, which are mapped and categorized depending on the degree of formality, structuredness, and level of engagement. In our study, three more common SE models in HPE policy and decision-making were investigated. Additionally, these collaborative methods emphasized curriculum development and quality assurance and employed students in these activities. It is worth mentioning that to make SE models more efficient and sustainable, several influencing factors and their interconnections should be considered.

Low Selenium and Low Protein Exacerbate Myocardial Damage in Keshan Disease by Affecting the PINK1/Parkin-mediated Mitochondrial Autophagy Pathway.

Keshan disease (KD) is a myocardial mitochondrial disease closely related to insufficient selenium (Se) and protein intake. PTEN induced putative kinase 1 (PINK1)/Parkin mediated mitochondrial autophagy regulates various physiological and pathological processes in the body. This study aimed to elucidate the relationship between PINK1/Parkin-regulated mitochondrial autophagy and KD-related myocardial injury. A low Se and low protein animal model was established. One hundred Wistar rats were randomly divided into 5 groups (control group, low Se group, low protein group, low Se + low protein group, and corn from KD area group). The JC-1 method was used to detect the mitochondrial membrane potential (MMP). ELISA was used to detect serum creatine kinase MB (CK-MB), cardiac troponin I (cTnI), and mitochondrial-glutamicoxalacetic transaminase (M-GOT) levels. RT-PCR and Western blot analysis were used to detect the expression of PINK1, Parkin, sequestome 1 (P62), and microtubule-associated proteins1A/1B light chain 3B (MAP1LC3B). The MMP was significantly decreased and the activity of CK-MB, cTnI, and M-GOT significantly increased in each experimental group (low Se group, low protein group, low Se + low protein group and corn from KD area group) compared with the control group (P<0.05 for all). The mRNA and protein expression levels of PINK1, Parkin and MAP1LC3B were profoundly increased, and those of P62 markedly decreased in the experimental groups compared with the control group (P<0.05 for all). Low Se and low protein levels exacerbate myocardial damage in KD by affecting the PINK1/Parkin-mediated mitochondrial autophagy pathway.

Emerging investigator series: predicted losses of sulfur and selenium in european soils using machine learning: a call for prudent model interrogation and selection.

Reductions in sulfur (S) atmospheric deposition in recent decades have been attributed to S deficiencies in crops. Similarly, global soil selenium (Se) concentrations were predicted to drop, particularly in Europe, due to increases in leaching attributed to increases in aridity. Given its international importance in agriculture, reductions of essential elements, including S and Se, in European soils could have important impacts on nutrition and human health. Our objectives were to model current soil S and Se levels in Europe and predict concentration changes for the 21st century. We interrogated four machine-learning (ML) techniques, but after critical evaluation, only outputs for linear support vector regression (Lin-SVR) models for S and Se and the multilayer perceptron model (MLP) for Se were consistent with known mechanisms reported in literature. Other models exhibited overfitting even when differences in training and testing performance were low or non-existent. Furthermore, our results highlight that similarly performing models based on RMSE or R2 can lead to drastically different predictions and conclusions, thus highlighting the need to interrogate machine learning models and to ensure they are consistent with known mechanisms reported in the literature. Both elements exhibited similar spatial patterns with predicted gains in Scandinavia versus losses in the central and Mediterranean regions of Europe, respectively, by the end of the 21st century for an extreme climate scenario. The median change was -5.5% for S (Lin-SVR) and -3.5% (MLP) and -4.0% (Lin-SVR) for Se. For both elements, modeled losses were driven by decreases in soil organic carbon, S and Se atmospheric deposition, and gains were driven by increases in evapotranspiration.

Bioenergy potential from agricultural by-product in 2030: An AI-based spatial analysis and climate change scenarios in a Chinese region

Addressing the pressing need for sustainable bioenergy sources amid the growing climate crisis, this study investigates potential crop bioenergy output in the Jianghan Plain, Hubei, China. The research leverages Backpropagation Artificial Neural Network models to predict the accumulated net primary productivity (NPP) of various crops, i.e., single rice, double rice, maize, and wheat, in Jianghan Plain. The models highlight a strong predictive performance, with the SE model achieving an R2 of 0.83 and wheat model an R2 of 0.90, implying these models can be utilized reliably for future NPP forecasting. Our evaluation of past and future NPP values reveals a negative correlation between the increase in NPP values and observed temperature trends. This emphasizes the significant impact of temperature on crop bioenergy potential. For future NPP predictions, scenario (S3a) under Temperature Control strategies at medium-effort mitigation yields a favourable outcome for all crops, particularly SE. Additionally, the results suggest that aggressive decarbonization and global warming mitigation strategies are vital for enhancing NPP values. However, the study also highlights potential trade-offs and stresses the necessity for region-specific climate reconciliation strategies; for instance, midland counties benefit from temperature control, whereas the sideline favours CO2 mitigation. The study further investigates the spatial distribution of potential bioenergy, with the southwestern part of the plain holding more bioenergy potential, providing an essential reference for future energy planning. By 2030, in Jianghan Plain, when fully integrated with reasonable climate responses, there could be substantial bioenergy potential from crop residues reaching (1.2–1.9) × 109 MJ, which is 11% less than the current.

Sustainable employability, technology acceptance and task performance in workers collaborating with cobots: a pilot study

Sustainable Employability (SE) and task performance of workers-collaborating-with-cobots is challenged. Whether SE policies can impact workers’ task performance in digitalized workplaces is still unknown. Drawing on two SE models, this study aims to ascertain whether the relationship between SE policies and task performance is mediated by health and productive capabilities, and whether this effect is moderated by the levels of User Acceptance and Use of Technology (UTAUT) predictors. 88 employees collaborating with cobots, or expected to do so in the near future, answered a cross-sectional survey. SE policies were positively related to task performance via health and productive capabilities. This indirect effect was moderated by the levels of UTAUT predictors, being significant only at low or medium levels of the moderators. SE policies contribute to employee capabilities, and in turn to workers’ task performance. Fostering health and productive capabilities is fundamental when employee levels of cobots’ acceptance are not high yet.

Paratransit service quality modeling reflecting users' perception-A case study in Dhaka, Bangladesh

Increasing usage of private cars and low service quality of public transport is an acute problem in many developing cities. The prerequisite is to meet the mobility needs, especially for low-income people, to ensure adequate capacity by the service provided. Paratransit is an indispensable mode of public transport, especially where the mass transit system is insufficient. Rapid increase in urban population, per capita income along existing transport infrastructure has stimulated their usage as a cheap and convenient public transport mode. Quality of service is considered as one of the most significant means to assess transit performance. To observe the performance of public transportation in roadway systems, overall passenger perceived service quality (SQ) has always been the most significant means of measurement. This research aims to establish a relationship between SQ variables describing the service of paratransit by Structural Equation Modeling (SEM) based on users' perceptions. An interview survey was conducted off-board to 2025 paratransit users at twenty paratransit routes in Dhaka metropolitan area (Bangladesh). Results show that the attribute of integration with supporting modes has the highest loading, inferring it as the most significant aspect of SQ. Attributes loading value may be explained according to the importance perceived by the users. Several SE models were developed using 21 service variables from 2000 questionnaires. Upon developing different models, the best model with three latent constructs was identified as the main characteristics for explaining the entire set of physical and service performance elements of the paratransit service. Three latent constructs were ‘Quality of transport’, ‘Transit performance’ and ‘Service quality’. Among 21 SQ variables, ‘Security of passenger’, ‘seat comfort level’, and ‘riding safety’ have been found to impart the greatest influence on the overall perceived SQ. The study findings support the data collected from paratransit users. This study may help the paratransit operators to determine variables that are decisive for SQ and their relation with the overall perceived SQ by the users. Understanding SQ variables, influencing passenger perception makes it easier to design and deliver good quality service.

Analysis of rock cuttability based on excavation parameters of TBM

Rock cuttability has great influence on the rock excavation efficiency of TBM (tunnel boring machine). In order to evaluate rock cuttability in real time, quickly, accurately and efficiently during TBM excavating, the relevant excavation parameters of Zagros, Kerman and Bazideraz tunnels were first collected. Then, the regression analyses between excavation parameters and rock cuttability were carried out. The two-dimensional regression analyses studied the relationship between operating parameters (thrust F and rotation speed RPM) and the characterization parameters (torque T and penetration rate PR). The three-dimensional regression analyses were utilized to create the PR and specific energy SE models based on operating parameters. The result shows that the established three-dimensional regression models have good prediction performance, and its performance is superior to two-dimensional models. Moreover, the prediction model of uniaxial compressive strength UCS and the classification model of rock cuttability were founded based on SE. The rock cuttability is divided into three levels, namely, easy (level 1), medium (level 2), and poor (level 3), in which the corresponding SE ranges are 0 to 6, 6 to 10 and exceeds 10 kWh·m−3, respectively. Finally, the intelligent algorithms, combined with excavation parameters, were introduced to establish UCS prediction model and rock cuttability classification model, and the good prediction performance was achieved. The above studies can provide necessary references and ideas for real-time, rapid, accurate and effective evaluation of rock cuttability based on TBM excavation parameters, and has certain guiding significance for engineering application.

Recognition of Edible Fungi Fruit Body Diseases Based on Improved ShuffleNetV2

Early recognition of fruit body diseases in edible fungi can effectively improve the quality and yield of edible fungi. This study proposes a method based on improved ShuffleNetV2 for edible fungi fruit body disease recognition. First, the ShuffleNetV2+SE model is constructed by deeply integrating the SE module with the ShuffleNetV2 network to make the network pay more attention to the target area and improve the model’s disease classification performance. Second, the network model is optimized and improved. To simplify the convolution operation, the 1 × 1 convolution layer after the 3 × 3 depth convolution layer is removed, and the ShuffleNetV2-Lite+SE model is established. The experimental results indicate that the accuracy, precision, recall, and Macro-F1 value of the ShuffleNetV2-Lite+SE model on the test set are, respectively, 96.19%, 96.43%, 96.07%, and 96.25%, which are 4.85, 4.89, 3.86, and 5.37 percent higher than those before improvement. Meanwhile, the number of model parameters and the average iteration time are 1.6 MB and 41 s, which is 0.2 MB higher and 4 s lower than that before the improvement, respectively. Compared with the common lightweight convolutional neural networks MobileNetV2, MobileNetV3, DenseNet, and EfficientNet, the proposed model achieves higher recognition accuracy, and its number of model parameters is significantly reduced. In addition, the average iteration time is reduced by 37.88%, 31.67%, 33.87%, and 42.25%, respectively. The ShuffleNetV2-Lite+SE model proposed in this paper has a good balance among performance, number of parameters, and real-time performance. It is suitable for deploying on resource-limited devices such as mobile terminals and helps in realization of real-time and accurate recognition of fruit body diseases of edible fungi.

Inflachromene attenuates seizure severity in mouse epilepsy models via inhibiting HMGB1 translocation.

Epilepsy is not well controlled by current anti-seizure drugs (ASDs). High mobility group box 1 (HMGB1) is a DNA-binding protein in the nucleus regulating transcriptional activity and maintaining chromatin structure and DNA repair. In epileptic brains, HMGB1 is released by activated glia and neurons, interacting with various receptors like Toll-like receptor 4 (TLR4) and downstream glutamatergic NMDA receptor, thus enhancing neural excitability. But there is a lack of small-molecule drugs targeting the HMGB1-related pathways. In this study we evaluated the therapeutic potential of inflachromene (ICM), an HMGB-targeting small-molecule inhibitor, in mouse epilepsy models. Pentylenetetrazol-, kainic acid- and kindling-induced epilepsy models were established in mice. The mice were pre-treated with ICM (3, 10 mg/kg, i.p.). We showed that ICM pretreatment significantly reduced the severity of epileptic seizures in all the three epilepsy models. ICM (10 mg/kg) exerted the most apparent anti-seizure effect in kainic acid-induced epileptic status (SE) model. By immunohistochemical analysis of brain sections from kainic acid-induced SE mice, we found that kainic acid greatly enhanced HMGB1 translocation in the hippocampus, which was attenuated by ICM pretreatment in subregion- and cell type-dependent manners. Notably, in CA1 region, the seizure focus, ICM pretreatment mainly inhibited HMGB1 translocation in microglia. Furthermore, the anti-seizure effect of ICM was related to HMGB1 targeting, as pre-injection of anti-HMGB1 monoclonal antibody (5 mg/kg, i.p.) blocked the seizure-suppressing effect of ICM in kainic acid-induced SE model. In addition, ICM pretreatment significantly alleviated pyramidal neuronal loss and granule cell dispersion in kainic acid-induced SE model. These results demonstrate that ICM is an HMGB-targeting small molecule with anti-seizure potential, which may help develop a potential drug for treating epilepsy.

The effect of pimobendan and enalapril use on blood serum trace element levels in dogs with myxomatous mitral valve disease

Background and objectiveMyxamatous mitral valve disease (MMVD) is a common acquired heart disease in dogs that can lead to progressive and long-term congestive heart failure (CHF). Pimobendan and angiotensin converting enzyme inhibitors (ACEI) are commonly used in MMVD treatment. Trace elements are essential for maintaining cardiovascular functions. Synergistic or antagonistic interactions between some trace elements and substances are widely known. In our study, it was aimed to evaluate the effects of treatment duration, age, weight and gender on serum trace element levels in dogs with MMVD by using pimobendan and enalapril as monotherapy. Material-Methods13 dogs with MMVD were treated with pimobendan (0.25–0.3 mg/kg, q12h, PO Vetmedin®) and 21 dogs with MMVD were treated with enalapril (0.25–0.5 mg/kg, q12h, PO Enapril®) for at least 30 days. The serum trace element levels (Cu, Zn, Fe, Co, Mg, Mn, Se, Cr) of all patients were measured with Inductively coupled plasma-Optical emission spectroscopy device. Relevances were evaluated related to treatment duration, age, weight and gender in each treatment group. ResultsAdjusted R2 values of the model, which included treatment, treatment duration, treatment x duration interaction, age, weight and gender as predictors, were negative except for Se. Furthermore, the model explained only 10.4% of the variation in Se. The effect of age was significant in the model of Se (β = 0.462; P = 0.028). Treatment duration also tended to be significant in the prediction of Se concentration (β = -0.364; P = 0.086). ConclusionWe consider that treatment (enalapril vs. pimobendan), treatment duration, treatment x duration interaction, weight and gender were not good predictors of trace elements. In our study, age contributed significantly only to the Se level.

Multiphysics factor facilitating fast reaction kinetics for high-power-density Li–Se batteries

An intriguing multiphysics factor for facilitating fast reaction kinetics is proposed herein to achieve high-power-density cathodes for lithium-selenium batteries (LSeBs) based on Se and Te-doped cathode models from the perspective of thermodynamic, chemical, and Li kinetics. Compared with the bare electrode, the optimized Te–Se electrode showed a much greater rate capability under harsh conditions in balancing the theoretical capacity and improving the redox kinetics. This fast kinetic property is understood in detail by cyclic voltammetry analysis, and a reduction in the electrochemical polarization, depending on the increase in the scan rate, is observed for the Te–Se electrode. In addition, the voltage hysteresis of the Se cathode increased rapidly in the C-rate mode, whereas that of the Te–Se electrode increased at a much slower rate. Therefore, a comparison of intrinsic thermodynamic hysteresis is performed to highlight the difference between the Se-based electrodes, and the reaction resistance and Li diffusion coefficient for the Te–Se cathode from the kinetic point of view are found to exhibit a superior feature during (dis)charging. The theoretical results are broken down into three perspectives: i) thermodynamics, ii) electronic structure, and iii) Li kinetics, systematically elucidating the fast kinetics of the Te-doped Se cathode compared to the pristine material. Lowering the formation energy, increasing the electronic population, and loosening the cation–anion bond are simultaneously triggered by Te doping, and their combined effects are deemed to be a multiphysics factor. The concrete experiment and computational understanding confirm the high power density of the Te–Se electrode relative to the reference model upon long-term cycling, and validate that the multiphysics factors pose a potentially global design strategy to enable fast kinetics for achieving high-performance LSeBs, even lithium-sulfur batteries.

Disease-modifying effects of sodium selenate in a model of drug-resistant, temporal lobe epilepsy.

There are no pharmacological disease-modifying treatments with an enduring effect to mitigate the seizures and comorbidities of established chronic temporal lobe epilepsy (TLE). This study aimed to evaluate for disease modifying effects of sodium selenate treatment in the chronically epileptic rat post-status epilepticus (SE) model of drug-resistant TLE. Wistar rats underwent kainic acid-induced SE or sham. Ten-weeks post-SE, animals received sodium selenate, levetiracetam, or vehicle subcutaneousinfusion continuously for 4 weeks. To evaluate the effects of the treatments, one week of continuous video-EEG was acquired before, during, and 4, 8 weeks post-treatment, followed by behavioral tests. Targeted and untargeted proteomics and metabolomics were performed on post-mortem brain tissue to identify potential pathways associated with modified disease outcomes. Telomere length was investigated as a novel surrogate marker of epilepsy disease severity in our current study. The results showed that sodium selenate treatment was associated with mitigation of measures of disease severity at 8 weeks post-treatment cessation; reducing the number of spontaneous seizures (p< 0.05), cognitive dysfunction (p< 0.05), and sensorimotor deficits (p< 0.01). Moreover, selenate treatment was associated with increased protein phosphatase 2A (PP2A) expression, reduced hyperphosphorylated tau, and reversed telomere length shortening (p< 0.05). Network medicine integration of multi-omics/pre-clinical outcomes identified protein-metabolite modules positively correlated with TLE. Our results provide evidence that treatment with sodium selenate results in a sustained disease-modifying effect in chronically epileptic rats in the post-KA SE model of TLE, including improved comorbid learning and memory deficits.

Abstract P303: Time-Varying Healthy Lifestyle Profile and All-Cause Mortality Among the Oldest-Old in China

Background: Association between an individual modifiable lifestyle with mortality is well established. However, influences of combined lifestyles on mortality risks remain inconclusive among adults aged 80 years or older (“oldest-old”). We investigated the association of time-varying healthy lifestyle profile (HLP) with mortality among Chinese oldest-old adults. Methods: We pooled 36,021 oldest-old adults from first seven waves of the Chinese Longitudinal Healthy Longevity Survey (1998-2014), with follow-up to latest wave (2018) (range 0-21.04 years). Lifestyle information was collected at each wave. Nonsmoking, no regular alcohol intake, regular exercise, and simplified healthy eating index of 11-18 were defined as healthy lifestyle. We assigned a score of 1 for healthy and 0 for nonhealthy lifestyle and summed the four scores to a HLP index ranging 0-5 (representing least to most healthy) to reflect joint lifestyle measurements. We used both a continuous time-to-event data set up with study enrollment as time origin (SE model) and a discrete time-to-event data set up with age 80 as time origin. We used Cox proportional hazard model with time-varying covariates and complementary log-log regression to examine hazard ratios (HRs) of mortality, adjusting for demographics, self-reported diseases, activities of daily living, and cognitive function. Results: During 110897.40 person-years of follow-up, 25,511 deaths were recorded. Median death age was 5 years older in most versus least healthy group. In SE model with least healthy group as reference, HRs (95% CIs) for mortality were 0.90 (0.86-0.95), 0.83 (0.79-0.87), 0.74 (0.70-0.78), 0.54 (0.49-0.58), respectively for HLP of 1-4 groups (p-trend&lt;0.001) ( Table ). Dose-response relationships were consistent for HRs of mortality over age since 80. Conclusion: Healthier lifestyle profiles were associated with significant reductions in all-cause mortality risks among Chinese oldest-old, suggesting the importance of life-long adherence to HLP into advanced old age.