A robust superhydrophobic brass mesh was fabricated based on a low-energy surface and a roughness on the nano/micro-meter scale. It was carried out by the forming of hydroxyapatite (HP) coatings on ...its surface through a constant current electro-deposition process, followed by immersion in fluoroalkylsilane solution. Surface morphology, composition and wetting behavior were investigated by field-emission scanning electron microscopy (SEM), X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), high speed camera, and contact angle goniometer. Under optimal conditions, the resulting brass mesh exhibited superhydrophobicity, excellent anti-corrosion (η = 91.2%), and anti-scaling properties. While the surfactant liquid droplets of tetradecyl trimethyl ammonium bromide (TTAB) with different concentration were dropped on the superhydrophobic surface, maximum droplet rebounding heights and different contact angles (CAs) were observed and measured from side-view imaging. The plots of surfactant-concentration−maximum bounding height/CA were constructed to determine its critical-micelle-concentration (CMC) value. Close CMC results of 1.91 and 2.32 mM based on the determination of maximum rebounding height and CAs were obtained. Compared with its theoretical value of 2.1 mM, the relative errors are 9% and 10%, respectively. This indicated that the novel application based on the maximum rebounding height could be an alternative approach for the CMC determination of other surfactants.
Long non-coding RNAs (lncRNAs) have been unveiled to play crucial parts in tumorigenesis and chemo-resistance of multiple cancers. Herein, we explored the role of NCK1-AS1 in ovarian cancer (OC). As ...indicated by TCGA, NCK1-AS1 was markedly upregulated in OC tissues. Besides, we found a dramatic upregulation of NCK1-AS1 in OC cell lines relative to the normal IOSE cells. Interestingly, silencing NCK1-AS1 confined cell proliferation, induced apoptosis and suppressed migration and invasion as well as enhanced DDP sensitivity in OC cells. As for mechanistic investigation, starBase (http://starbase.sysu.edu.cn/) suggested that NCK1-AS1 expression in OC tissues was significantly positively correlated with its adjacent gene, NCK adaptor protein 1 (NCK1). Furtherly, we demonstrated that the cytoplasmic NCK1-AS1 competed with NCK1 mRNA for miR-137 binding to boost NCK1 mRNA expression. Importantly, miR-137 inhibition could only offset the suppression of NCK1-AS1 depletion on NCK1 mRNA level but not the protein level. Moreover, NCK1-AS1 stabilized NCK1 protein by hindering c-Cbl-induced degradation via directly interacting with c-Cbl. Furthermore, NCK1 upregulation reversed the influences of NCK1-AS1 inhibition on the biological behaviors and DDP resistance of OC cells. This study disclosed a NCK1-AS1/NCK1 axis in regulating OC progression and chemo-resistance, opening a new path for treatment and chemo-resistance overcoming in OC.
•NCK1-AS1 accelerates cell proliferation, migration and invasion as well as chemo-resistance formation in ovarian cancer.•NCK1-AS1 elevates NCK1 mRNA expression by interacting with miR-137.•NCK1-AS1 prevents NCK1 protein from c-Cbl-mediated degradation through competitively interacting with c-Cbl.•NCK1-AS1 contributes to carcinogenesis and chemo-resistance of ovarian cancer through targeting NCK1.
Global warming threatens many aspects of human life, for example, by reducing crop yields. Breeding heat-tolerant crops using genes conferring thermotolerance is a fundamental way to help deal with ...this challenge. Here we identify a major quantitative trait locus (QTL) for thermotolerance in African rice (Oryza glaberrima), Thermo-tolerance 1 (TT1), which encodes an α2 subunit of the 26S proteasome involved in the degradation of ubiquitinated proteins. Ubiquitylome analysis indicated that OgTT1 protects cells from heat stress through more efficient elimination of cytotoxic denatured proteins and more effective maintenance of heat-response processes than achieved with OsTT1. Variation in TT1 has been selected for on the basis of climatic temperature and has had an important role in local adaptation during rice evolution. In addition, we found that overexpression of OgTT1 was associated with markedly enhanced thermotolerance in rice, Arabidopsis and Festuca elata. This discovery may lead to an increase in crop security in the face of the ongoing threat of global warming.
Nonlinearity and stochasticity are two important factors contributing to the degradation processes of complicated systems, and thus have to be taken into account in stochastic degradation modeling ...based prognostics. However, current studies almost always focus on age-dependent stochastic degradation models, most of which are linear, or can be transformed into linear models. In this paper, we propose a general age- and state-dependent nonlinear degradation model for prognostics. In the presented model, a diffusion process with age- and state-dependent nonlinear drift and volatility coefficients is utilized to characterize the dynamics and nonlinearity of the degradation progression. To derive the estimated remaining useful life distribution, the considered diffusion process is first converted into a diffusion process with age- or state-dependent nonlinear drift but constant volatility through Lamperti transformation. Then, based on a well-known time-space transformation, we obtain an analytical approximated remaining useful life distribution in the concept of the first passage time. Furthermore, a maximum likelihood estimation method for unknown parameters in the concerned model is presented on the basis of closed-form approximated degradation state transition density functions by the Hermite-expansion method. An illustrative example is provided to show how the obtained results can be applied to a specific age- and state-dependent nonlinear degradation model. Finally, the presented model is fitted to bearing degradation data. Comparative results suggest the necessity of age- and state-dependent nonlinear degradation modeling in prognostics.
A developed Fourier transform infrared spectroscopy (FT-IR) was employed to investigate changes of protein conformation, which played significant roles in maintaining stable protein networks of white ...croaker surimi gel, exploring the relationship between protein conformation and surimi gel networks. Spectra of surimi and gels with different grades (A, AA, FA and SA) were analyzed by tri-step FT-IR method and peak-fitting of deconvolved and baseline corrected amide I bands (1600~1700 cm
). The result showed that α-helix was the main conformation of surimi proteins. During surimi gelation, α-helix of myosin partially transformed into β-sheet, β-turn and random coil structures. β-sheet and random coil structures were the main protein conformations maintaining the structure of surimi gel, of which β-sheet made the main contribution to gel strength. Scanning electron microscopy (SEM) result revealed that surimi gels had a fibrous and homogeneous network structure. Moreover, ordered interconnections between three-dimensional proteins networks of gels were inclined to emerge in higher grade surimi, in agreement with the gel strength results. It was demonstrated that the tri-step FT-IR spectroscopy combined with peak-fitting could be applicable for exploration of surimi protein conformation changes during gelation to deepen understanding of its effect on gel quality.
Item compromise persists in undermining the integrity of testing, even secure administrations of computerized adaptive testing (CAT) with sophisticated item exposure controls. In ongoing efforts to ...tackle this perennial security issue in CAT, a couple of recent studies investigated sequential procedures for detecting compromised items, in which a significant increase in the proportion of correct responses for each item in the pool is monitored in real time using moving averages. In addition to actual responses, response times are valuable information with tremendous potential to reveal items that may have been leaked. Specifically, examinees that have preknowledge of an item would likely respond more quickly to it than those who do not. Therefore, the current study proposes several augmented methods for the detection of compromised items, all involving simultaneous monitoring of changes in both the proportion correct and average response time for every item using various moving average strategies. Simulation results with an operational item pool indicate that, compared to the analysis of responses alone, utilizing response times can afford marked improvements in detection power with fewer false positives.
Among 176 patients who had had severe acute respiratory syndrome (SARS), SARS-specific antibodies were maintained for an average of 2 years, and significant reduction of immunoglobulin G-positive ...percentage and titers occurred in the third year. Thus, SARS patients might be susceptible to reinfection >or=3 years after initial exposure.
Degradation-model-based remaining useful life (RUL) estimation is essential for effective prognostic and health management; this method can provide information for enabling effective maintenance ...decisions pertaining to degrading systems to avoid or mitigate loss due to impending failures. According to existing studies that estimate degradation-model-based RUL, stochastic-process-based methods are widely advocated by many researchers because they can capture stochastic dynamics within the degradation processes of systems. However, most existing studies primarily utilize the first passage time (FPT) to define the lifetime/RUL. The definition is generally conservative; thus, the lifetime/RUL estimation may be underestimated. This is particularly true for nonmonotonic stochastic degradation processes. In some extreme cases, with strong fluctuations within the degradation processes, the estimated lifetime/RUL under the FPT can be significantly less than the actual lifetime/RUL. To address this limitation, this study investigates prognostic issues, based on the stochastic degradation process, from the last exit time (LET) perspective. In contrast to the FPT, the lifetime/RUL of the degrading system is defined as the LET of its degradation process, i.e., the instant at which the degradation process passes the failure threshold for the last time. Given the new definition, we consider the most widely used degradation process model (i.e., Wiener-process-based model) as an example to demonstrate how the lifetime/RUL is estimated. Two general methods of lifetime estimation for the Wiener-process-based model are given, and some examples with associated exact and closed-form solutions are also provided for better illustration. Finally, numerical examples and a practical case study are presented to substantiate the theoretical results and illustrate the superiority of the proposed method. The results imply that the proposed method exhibits the potential to prevent premature maintenance and resource wastage because the lifetime/RUL estimation from the LET perspective can help avoid conservative results from being obtained.
The COVID-19 pandemic significantly disrupted nursing home (NH) care, including visitation restrictions, reduced staffing levels, and changes in routine care. These challenges may have led to ...increased behavioral symptoms, depression symptoms, and central nervous system (CNS)-active medication use among long-stay NH residents with dementia.
We conducted a retrospective, cross-sectional study including Michigan long-stay (≥100 days) NH residents aged ≥65 with dementia based on Minimum Data Set (MDS) assessments from January 1, 2018 to June 30, 2021. Residents with schizophrenia, Tourette syndrome, or Huntington's disease were excluded. Outcomes were the monthly prevalence of behavioral symptoms (i.e., Agitated Reactive Behavior Scale ≥ 1), depression symptoms (i.e., Patient Health Questionnaire PHQ-9 ≥ 10, reflecting at least moderate depression), and CNS-active medication use (e.g., antipsychotics). Demographic, clinical, and facility characteristics were included. Using an interrupted time series design, we compared outcomes over two periods: Period 1: January 1, 2018-February 28, 2020 (pre-COVID-19) and Period 2: March 1, 2020-June 30, 2021 (during COVID-19).
We included 37,427 Michigan long-stay NH residents with dementia. The majority were female, 80 years or older, White, and resided in a for-profit NH facility. The percent of NH residents with moderate depression symptoms increased during COVID-19 compared to pre-COVID-19 (4.0% vs 2.9%, slope change SC = 0.03, p < 0.05). Antidepressant, antianxiety, antipsychotic and opioid use increased during COVID-19 compared to pre-COVID-19 (SC = 0.41, p < 0.001, SC = 0.17, p < 0.001, SC = 0.07, p < 0.05, and SC = 0.24, p < 0.001, respectively). No significant changes in hypnotic use or behavioral symptoms were observed.
Michigan long-stay NH residents with dementia had a higher prevalence of depression symptoms and CNS active-medication use during the COVID-19 pandemic than before. During periods of increased isolation, facility-level policies to regularly assess depression symptoms and appropriate CNS-active medication use are warranted.
Many industrial systems inevitably suffer performance degradation. Thus, predicting the remaining useful life (RUL) for such degrading systems has attracted significant attention in the prognostics ...community. For some systems like batteries, one commonly encountered phenomenon is that the system performance degrades with usage and recovers in storage. However, almost all of the current prognostic studies do not consider such a recovery phenomenon in stochastic degradation modeling. In this paper, we present a prognostic model for deteriorating systems experiencing a switching operating process between usage and storage, where the system degradation state recovers randomly after the storage process. The possible recovery from the current time to the predicted future failure time is incorporated in the prognosis. First, the degradation state evolution of the system is modeled through a diffusion process with piecewise but time-dependent drift coefficient functions. Under the concept of first hitting time, we derived the lifetime and RUL distributions for systems with specific constant working mode. Further, we extended the results of RUL distribution in specific constant working mode to the case of stochastic working mode, which is modeled through a flexible two-state semi-Markov model (SMM) with phase-type distributed interval times. The unknown parameters in the present model are estimated based on the observed condition monitoring data of the system, and the SMM model is identified on the basis of the operating data. A numerical study and a case study of Li-ion batteries are carried out to illustrate and demonstrate the proposed prognostic method. Experimental results indicate that the presented method can improve the accuracy of lifetime and RUL estimation for systems with state recovery.
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