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Surface-enhanced Raman spectroscopy (SERS) is advantageous over fluorescence for bioimaging due to ultra-narrow linewidth of the fingerprint spectrum and weak photo-bleaching effect. However, the existing SERS imaging speed lags far behind practical needs, mainly limited by Raman signals of SERS nanoprobes. In this work, we report ultrabright gap-enhanced Raman tags (GERTs) with strong electromagnetic hot spots from interior sub-nanometer gaps and external petal-like shell structures, larger immobilization surface area, and Raman cross section of reporter molecules. These GERTs reach a Raman enhancement factor beyond 5 × 10 9 and a detection sensitivity down to a single-nanoparticle level. We use a 370 μW laser to realize high-resolution cell imaging within 6 s and high-contrast (a signal-to-background ratio of 80) wide-area (3.2 × 2.8 cm 2 ) sentinel lymph node imaging within 52 s. These nanoprobes offer a potential solution to overcome the current bottleneck in the field of SERS-based bioimaging. The speed of surface-enhanced Raman spectroscopy (SERS) imaging is generally limited due to low Raman signals. Here, the authors develop bright gap-enhanced Raman tags with external hot spots and demonstrate their use in fast near-infrared bioimaging.

Anticounterfeiting labels based on physical unclonable functions (PUFs), as one of the powerful tools against counterfeiting, are easy to generate but difficult to duplicate due to inherent randomness. Gap-enhanced Raman tags (GERTs) with embedded Raman reporters show strong intensity enhancement and ultra-high photostability suitable for fast and repeated readout of PUF labels. Herein, we demonstrate a PUF label fabricated by drop-casting aqueous GERTs, high-speed read using a confocal Raman system, digitized through coarse-grained coding methods, and authenticated via pixel-by-pixel comparison. A three-dimensional encoding capacity of over 3 × 10 15051 can be achieved for the labels composed of ten types of GERTs with a mapping resolution of 2500 pixels and quaternary encoding of Raman intensity levels at each pixel. Authentication experiments have ensured the robustness and security of the PUF system, and the practical viability is demonstrated. Such PUF labels could provide a potential platform to realize unbreakable anticounterfeiting. Physical unclonable functions with inherent randomness are promising candidates for secure labeling systems. Here the authors demonstrate such a function using gap-enhanced Raman tags to create high-capacity and high-security labels for anticounterfeiting.

Today, energy management based on the digitalization of smart grids by the Internet of Things (IoT) is an emerging paradigm for power line systems. There are several environmental hazards to break down high-voltage power cables such as lightning, severe voltage fluctuations, and incorrect design of electric field distribution. So, identifying faulty high-voltage power lines is one of the most emerging challenges in smart grids to avoid disruption of the power distribution networks. This paper presents a new hybrid Convolutional Neural Network and Relief-F (CNN-RF) algorithm for an energy-aware collaborative learning approach to detect power line systems in smart grids. This hybrid approach ensures the stability and reliability of the defective power line system and improves the energy efficiency of the smart grids. This approach can detect the defective power line recognition using damaged power line images concerning automatic monitoring using Unmanned Aerial Vehicle (UAV) control system and IoT communications. By applying UAV control system and IoT communications on gathering damaged power line images, human faults and environmental hazards for extra data transmission are avoided. Experimental results show that the proposed CNN-RF model represents a high accuracy rate of 92.2% for recognizing damaged power lines. Also, the precision of damaged line detection ratio is higher than other prediction methods by the rate of 92.5%. Finally, the performance of the damaged line prediction approach in the CNN-RF method has a daily minimum cost in the IoT-based smart grids.

The benefit of integrating batches of genomic data to increase statistical power is often hindered by batch effects, or unwanted variation in data caused by differences in technical factors across batches. It is therefore critical to effectively address batch effects in genomic data to overcome these challenges. Many existing methods for batch effects adjustment assume the data follow a continuous, bell-shaped Gaussian distribution. However in RNA-seq studies the data are typically skewed, over-dispersed counts, so this assumption is not appropriate and may lead to erroneous results. Negative binomial regression models have been used previously to better capture the properties of counts. We developed a batch correction method, ComBat-seq, using a negative binomial regression model that retains the integer nature of count data in RNA-seq studies, making the batch adjusted data compatible with common differential expression software packages that require integer counts. We show in realistic simulations that the ComBat-seq adjusted data results in better statistical power and control of false positives in differential expression compared to data adjusted by the other available methods. We further demonstrated in a real data example that ComBat-seq successfully removes batch effects and recovers the biological signal in the data.

Major depressive disorder is one of the most common mental disorders in children and adolescents. However, whether to use pharmacological interventions in this population and which drug should be preferred are still matters of controversy. Consequently, we aimed to compare and rank antidepressants and placebo for major depressive disorder in young people. We did a network meta-analysis to identify both direct and indirect evidence from relevant trials. We searched PubMed, the Cochrane Library, Web of Science, Embase, CINAHL, PsycINFO, LiLACS, regulatory agencies' websites, and international registers for published and unpublished, double-blind randomised controlled trials up to May 31, 2015, for the acute treatment of major depressive disorder in children and adolescents. We included trials of amitriptyline, citalopram, clomipramine, desipramine, duloxetine, escitalopram, fluoxetine, imipramine, mirtazapine, nefazodone, nortriptyline, paroxetine, sertraline, and venlafaxine. Trials recruiting participants with treatment-resistant depression, treatment duration of less than 4 weeks, or an overall sample size of less than ten patients were excluded. We extracted the relevant information from the published reports with a predefined data extraction sheet, and assessed the risk of bias with the Cochrane risk of bias tool. The primary outcomes were efficacy (change in depressive symptoms) and tolerability (discontinuations due to adverse events). We did pair-wise meta-analyses using the random-effects model and then did a random-effects network meta-analysis within a Bayesian framework. We assessed the quality of evidence contributing to each network estimate using the GRADE framework. This study is registered with PROSPERO, number CRD42015016023. We deemed 34 trials eligible, including 5260 participants and 14 antidepressant treatments. The quality of evidence was rated as very low in most comparisons. For efficacy, only fluoxetine was statistically significantly more effective than placebo (standardised mean difference −0·51, 95% credible interval [CrI] −0·99 to −0·03). In terms of tolerability, fluoxetine was also better than duloxetine (odds ratio [OR] 0·31, 95% CrI 0·13 to 0·95) and imipramine (0·23, 0·04 to 0·78). Patients given imipramine, venlafaxine, and duloxetine had more discontinuations due to adverse events than did those given placebo (5·49, 1·96 to 20·86; 3·19, 1·01 to 18·70; and 2·80, 1·20 to 9·42, respectively). In terms of heterogeneity, the global I2 values were 33·21% for efficacy and 0% for tolerability. When considering the risk–benefit profile of antidepressants in the acute treatment of major depressive disorder, these drugs do not seem to offer a clear advantage for children and adolescents. Fluoxetine is probably the best option to consider when a pharmacological treatment is indicated. National Basic Research Program of China (973 Program).

Person re-identification (ReID) technology has many applications in intelligent surveillance and public safety. However, the domain difference between the source and target domains makes the generalization ability of the model extremely challenging. To reduce the dependence on labeled data, Unsupervised Domain Adaptation (UDA) methods have become an effective way to solve this problem. However, the influence of pseudo-label generated noise on model training in existing UDA methods is still significant, resulting in limited model performance on the target domain. For this reason, this paper proposes a contrast learning-based pseudo-label refinement with probabilistic uncertainty in the unsupervised domain, adapted to Person re-identification, aiming to improve the effectiveness of the unsupervised domain adapted to Person re-identification. We first enhance the feature representation of the target domain samples based on the contrast learning technique to improve their discrimination in the feature space, thereby enhancing the cross-domain migration performance of the model. Subsequently, an innovative loss function is proposed to effectively reduce the interference of label noise on the training process by refining the generation process of pseudo-labels, which solves the negative impact of inaccurate pseudo-labels on model training. Through a series of experimental validation, the method experiments on two large-scale public datasets, Market1501 and DukeMTMC, and the Rank-1 accuracy of the proposed method reaches 91.4% and 81.4%, with the mean average precision (mAP) of 79.0% and 67.9%, respectively, which proves that the research in this paper provides a good solution for the Person re-identification task with effective technical support for label noise processing and model generalization capability improvement.

Background Current research has focused on exploring the sources of parents’ anxiety about children’s education ( PAE ), and we continue in this direction by exploring the factors influencing parental educational anxiety in primary and secondary school students and the interactions among them. Methods Parental Educational Anxiety Measurement Questionnaire was used to measure the level of PEA . Pearson correlation coefficient analysis was used to examine the correlation between the level of PAE and the demographic variables. The method of multiple stepwise regression analysis was used to explore the demographic factors correlated with PAE . Two-way interactions in moderated multiple regression to examine the moderating effects of educational attainment on monthly household mortgage payments and PAE . Results Our results indicate that there were statistical differences among education level, average monthly household income, child’s stage of learning and monthly household mortgage payments; PEA were negatively correlated with education level and average monthly household income, and positively correlated with monthly cost of educational inputs. The results of multiple regression analysis showed that education level, average monthly household income, monthly household mortgage payment, and monthly cost of educational inputs were direct influences on PEA. Education level has a significant moderating effect on the monthly mortgage payment and PEA . Conclusion Education level, average monthly household income, monthly household mortgage payment, and monthly cost of educational inputs were direct influencing factor of PEA.

Urban ventilation corridors introduce fresh air into urban interiors and improve urban livability, while mitigating the urban heat island (UHI) effect. However, few studies have assessed the impact of urban ventilation corridors on UHI intensity (UHII) from the perspective of the local climates of different cities. Therefore, this study integrated multisource data to construct ventilation corridors from the perspective of local climate zone (LCZ) and analyzed its impact on UHII. The results showed the following: (1) the average UHII of constructed LCZs was higher than that of natural LCZs, among which the building type LCZ10 (heavy industry) had the highest intensity (5.77 °C); (2) in extracted ventilation corridors, the pixel number of natural LCZs was substantially larger than that of constructed LCZs, among which LCZE (bare soil/paved) was the largest; and (3) for natural LCZs, the average UHII of each LCZ was lower within the ventilated corridors than within the non-ventilated corridors (except for LCZG [water]), with the UHII of LCZB (scattered trees) exhibiting the greatest mitigation effect. Quantitative research on the composition and function of ventilation corridors can not only assess the ability of ventilation corridors to mitigate UHIs, but also provide a reference for urban ventilation corridor planning.

To determine quantitative differences between weight loss and changes in clinic blood pressure (BP) and ambulatory BP in patients with obesity or overweight, the authors performed a meta‐analysis. PubMed, Embase, and Scopus databases were searched up to June 2022. Studies that compared clinic or ambulatory BP with weight loss were included. A random effect model was applied to pool the differences between clinic BP and ambulatory BP. Thirty‐five studies, for a total of 3219 patients were included in this meta‐analysis. The clinic systolic blood pressure (SBP) and diastolic blood pressure (DBP) were significantly reduced by 5.79 mmHg (95% CI, 3.54–8.05) and 3.36 mmHg (95% CI, 1.93–4.75) after a mean body mass index (BMI) reduction of 2.27 kg/m2, and the SBP and DBP were significantly reduced by 6.65 mmHg (95% CI, 5.16–8.14) and 3.63 mmHg (95% CI, 2.03–5.24) after a mean BMI reduction of 4.12 kg/m2. The BP reductions were much larger in patients with a BMI decrease ≥3 kg/m2 than in patients with less BMI decrease, both for clinic SBP [8.54 mmHg (95% CI, 4.62–12.47)] versus [3.83 mmHg (95% CI, 1.22–6.45)] and clinic DBP [3.45 mmHg (95% CI, 1.59–5.30)] versus [3.15 mmHg (95% CI, 1.21–5.10)]. The significant reduction of the clinic and ambulatory BP followed the weight loss, and this phenomenon could be more notable after medical intervention and a larger weight loss.

The clinical challenges in monitoring high-incidence complications in patients with colostomy after colorectal cancer surgery have led to the development of an intelligent monitoring system based on deep learning and augmented reality technology in this study. Traditional care relies on subjective scales for assessment, which has issues such as insufficient sensitivity and delayed response. The existing technical systems also have significant limitations in perception dimensions, environmental adaptability, and decision-making timeliness. Therefore, this study proposes a triple-technology integration solution: by fusing impedance sensing, pH-responsive hydrogels, and 3D depth cameras in a heterogeneous manner, a multi-modal perception network is established to achieve the collaborative collection of physiological and biochemical parameters and morphological features; a cross-modal spatio-temporal fusion network is designed, using dynamic attention mechanisms and differential manifold optimization algorithms to solve the feature coupling problem caused by the heterogeneity of multi-source data; an augmented reality visualization system is developed, combining spatial projection optimization and programmable haptic feedback to build a “visual-tactile-spatial” multi-dimensional human–machine interaction channel. The system was verified through multi-center randomized controlled trials, confirming its significant improvement in the early warning performance of complications, enhancement of the standardization of nursing operations, and reduction of medical resource consumption. The core innovation lies in the first introduction of the federated learning framework into the field of colostomy care, combined with neural architecture search for lightweight model deployment, and the quantification of health economic benefits through Markov models. This research marks a paradigm shift in postoperative care from experience-driven to data-driven, providing an extensible technical model for the intelligent monitoring of chronic diseases. In the future, it is necessary to expand the cross-disease transfer learning architecture and develop flexible and degradable sensors to enhance the accessibility of primary medical care.