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BackgroundThe existing tools in China for assessing psychiatric nurses' knowledge， attitude and practice towards the use of protective restraint do not sufficiently consider the specialty in psychiatric practice， and the scale items are somewhat cumbersome to use， which together restrict their further promotion and application. Accordingly， there is an urgent need for developing a more scientific assessment tool.ObjectiveTo construct a scale for assessing psychiatric nurses' knowledge， attitude and practice towards the use of protective restraint and to verify its reliability and validity， so as to provide a generic tool for the assessment.MethodsBased on the theoretical framework of the knowledge-attitude-practice model， the preliminary scale was formed through systematic literature review， qualitative interview and Delphi method. From July to December 2021， a stratified sampling was utilized to select 729 psychiatric nurses from tertiary， secondary and primary （including unclassified medical institutions and grassroots hospitals） psychiatric hospitals in Guangdong Province. The formal scale was developed through item analysis， exploratory factor analysis and confirmatory factor analysis. The validity of the scale was evaluated by content validity and structure validity， and the reliability was verified by procedures including Cronbach's α coefficient， test-retest reliability， and split-half reliability.ResultsA total of 12 items of three dimensions （knowledge， attitude， practice） were included in the scale for assessing psychiatric nurses' knowledge， attitude and practice towards the use of protective restraint. The scale-level content validity index （S-CVI） was 0.941， and the item-level content validity index （I-CVI） ranged from 0.812 to 1.000. Exploratory factor analysis extracted three common factors， and the cumulative variance contribution rate was 62.948%. The confirmatory factor yielded adequate fit. Cronbach's α coefficient was found to be 0.887 for the scale， 0.819 for knowledge dimension， 0.842 for attitude dimension， and 0.831 for practice dimension. The split-half reliability and test-retest reliability for the scale were 0.712 and 0.922， respectively.ConclusionThe scale shows satisfactory reliability and validity， which can be used to assess psychiatric nurses' knowledge， attitude and practice towards the use of protective restraint. ［Funded by Guangdong Clinical Teaching Base Teaching Reform Research Project （number， 2021JD119）］

Electrical stimulation is an important adjuvant therapy for spinal surgery, but whether receiving electrical stimulation can improve the fusion rate after spinal surgery is still controversial. The purpose of this study was to analyse and evaluate the effect of electrical stimulation on the fusion rate after spinal surgery. We systematically searched for related articles published in the PubMed, Embase and Cochrane Library databases on or before September 30, 2023. The odds ratio (OR) with 95% confidence interval (CI) and the fusion rates of the experimental group and the control group were calculated by a random-effects meta-analysis model. The analysis showed that receiving electrical stimulation significantly increased the probability of successful spinal fusion (OR 2.66 [95% CI 1.79–3.97]), and the average fusion rate of the electrical stimulation group (86.8%) was significantly greater than that of the control group (73.7%). The fusion rate in the direct current (DC) stimulation group was 2.33 times greater than that in the control group (OR 2.33 [95% CI 1.37–3.96]), and that in the pulsed electromagnetic field (PEMF) group was 2.60 times greater than that in the control group (OR 2.60 [95% CI 1.29–5.27]). Similarly, the fusion rate in the capacitive coupling (CC) electrical stimulation group was 3.44 times greater than that in the control group (OR 3.44 [95% CI 1.75–6.75]), indicating that regardless of the type of electrical stimulation, the fusion rate after spinal surgery improved to a certain extent. Electrical stimulation as an adjuvant therapy seems to improve the fusion rate after spinal surgery to a certain extent, but the specific effectiveness of this therapy needs to be further studied. [ABSTRACT FROM AUTHOR]

Cold acclimation before winter has been shown to enhance the cold tolerance of evergreen conifers, including Pinus massoniana Lamb., a characteristic heteroblastic foliage tree in the conifer. In the initial growing season of P. massoniana, both primary needle seedlings (PNSs) and secondary needle seedlings (SNSs) are generated. While previous research has highlighted differences in the morphological structure and photosynthetic physiological functions of primary and secondary needles, their response to cold acclimation remains poorly understood. This study aimed to investigate the changes in morpho-anatomical structure, starch grain accumulation, and lignin deposition in the roots, stems, and leaves of PNSs and SNSs during cold acclimation using solid potassium iodide and hydrochloric acid phloroglucinol double-staining techniques. The results revealed that, during cold acclimation, the leaves and stems of PNSs exhibited sensitivity to low-temperature stress, resulting in noticeable shrinkage and fracture of mesophyll and cortical parenchyma cells. Furthermore, the early stages of cold acclimation promoted the accumulation of starch grains and lignin in the seedling tissues. In contrast to PNSs, the leaves and stems of SNSs exhibited a shorter cold acclimation period, attributed to the hydrolysis of starch grains in the epidermal cell walls and the transformation of xylem lignin, which supports cell structure stability and enhances cold resistance. In conclusion, these findings suggest that SNSs displayed a superior cold resistance potential compared to PNSs following cold acclimation, providing a significant theoretical basis for the further screening of cold-tolerant germplasm resources of P. massoniana and the analysis of cold resistance traits in heteroblastic foliage. [ABSTRACT FROM AUTHOR]

Pinus massoniana Lamb. is an evergreen conifer; however, some current-year seedlings exhibit "winter reddening" at the onset of winter. The biological significance of this reddening is unclear. We examine the physiological responses of needles during the reddening process and explore the relationships between physiological traits and seeding cold resistance. Based on needle color, we recognize non-reddened, partially reddened, and fully reddened needle stages. As reddening progresses, chlorophyll fluorescence parameters (maximum light energy conversion efficiency of photosystem II (PSII) photochemistry, PSII potential activity, effective photosynthetic quantum yield, non-photochemical and photochemical quenching coefficients, and actual quantum yield of PSII photochemistry) decrease, reducing photosynthetic efficiency. Concurrently, the proportion of regulated energy dissipation in quantum yield of PSII decreases, and that of PSII non-regulated energy dissipation increases. Antioxidant enzyme activities (catalase and peroxidase) and osmoregulatory substances (soluble sugars and proteins and proline) increase, and malondialdehyde levels and relative cell damage at 4 °C and −10 °C gradually increase. Although P. massoniana seedlings adapt to low-temperature environments as their needles redden by increasing antioxidant enzyme activities and osmoregulatory substances and by adjusting photosynthetic efficiency and light energy distribution, cell membrane damage persists. Cold resistance in P. massoniana seedlings is not fully established during winter reddening. [ABSTRACT FROM AUTHOR]

Current methods for detecting apple watercore are expensive and potentially damaging to the fruit. To determine whether different batches of apples are suitable for long-term storage or long-distance transportation, and to classify the apples according to quality level to enhance the economic benefits of the apple industry, it is essential to conduct non-destructive testing for watercore. This study proposes an innovative detection method based on optical parameter inversion and the MobileNetV3 model. Initially, a three-layer plate model of apples was constructed using the Monte Carlo method to simulate the movement of photons inside the apple, generating a simulated brightness map of photons on the apple's surface. This map was then used to train the MobileNetV3 network with dilated convolution, resulting in a pre-trained model. Through transfer learning, this model was applied to measured spectral data to detect the presence of watercore. Comparative experiments were conducted to determine the optimal transfer strategy for the frozen layers, achieving model accuracy rates of 99.13%, 97.60%, and 95.32% for two, three, and four classifications, respectively. Furthermore, the model parameters were low at 7.52 M. Test results of this study confirmed the effectiveness and lightweight characteristics of the method that combines optical property parameter inversion, the DC-MobileNetV3 model, and transfer learning for detecting apple watercore. This model provides technical support to detect watercore and other internal diseases in apples. [ABSTRACT FROM AUTHOR]

Change‐point detection studies the problem of detecting the changes in the underlying distribution of the data stream as soon as possible after the change happens. Modern large‐scale, high‐dimensional, and complex streaming data call for computationally (memory) efficient sequential change‐point detection algorithms that are also statistically powerful. This gives rise to a computation versus statistical power trade‐off, an aspect less emphasized in the past in classic literature. This tutorial takes this new perspective and reviews several sequential change‐point detection procedures, ranging from classic sequential change‐point detection algorithms to more recent non‐parametric procedures that consider computation, memory efficiency, and model robustness in the algorithm design. Our survey also contains classic performance analysis, which provides useful techniques for analyzing new procedures. This article is categorized under:Statistical Models > Time Series ModelsAlgorithms and Computational Methods > AlgorithmsData: Types and Structure > Time Series, Stochastic Processes, and Functional Data [ABSTRACT FROM AUTHOR]