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Abstract Diagnostic pathology, historically dependent on visual scrutiny by experts, is essential for disease detection. Advances in digital pathology and developments in computer vision technology have led to the application of artificial intelligence (AI) in this field. Despite these advancements, the variability in pathologists’ subjective interpretations of diagnostic criteria can lead to inconsistent outcomes. To meet the need for precision in cancer therapies, there is an increasing demand for accurate pathological diagnoses. Consequently, traditional diagnostic pathology is evolving towards “next-generation diagnostic pathology”, prioritizing on the development of a multi-dimensional, intelligent diagnostic approach. Using nonlinear optical effects arising from the interaction of light with biological tissues, multiphoton microscopy (MPM) enables high-resolution label-free imaging of multiple intrinsic components across various human pathological tissues. AI-empowered MPM further improves the accuracy and efficiency of diagnosis, holding promise for providing auxiliary pathology diagnostic methods based on multiphoton diagnostic criteria. In this review, we systematically outline the applications of MPM in pathological diagnosis across various human diseases, and summarize common multiphoton diagnostic features. Moreover, we examine the significant role of AI in enhancing multiphoton pathological diagnosis, including aspects such as image preprocessing, refined differential diagnosis, and the prognostication of outcomes. We also discuss the challenges and perspectives faced by the integration of MPM and AI, encompassing equipment, datasets, analytical models, and integration into the existing clinical pathways. Finally, the review explores the synergy between AI and label-free MPM to forge novel diagnostic frameworks, aiming to accelerate the adoption and implementation of intelligent multiphoton pathology systems in clinical settings.

In recent years, notable advancements have been achieved in the realm of identifying IP-based Internet of Things (IoT) devices and events. Nevertheless, the majority of methods rely on extracting fingerprints or features from plain text IP-based packets, which limits their ability to accommodate heterogeneous IoT devices such as ZigBee and Z-Wave, and fails to address the challenge of limited traffic samples. To tackle these issues, we propose a novel approach based on IoT communication characteristics and featuring module extensibility. This method is presented to effectively identify IoT devices and events from non-IP heterogeneous IoT network traffic. To shield the differences caused by the heterogeneous IoT protocol, a heterogeneous sample extraction platform with an extensible structure is created to extract raw sequence samples from ZigBee and Z-Wave traffic, with potential for expansion to other protocols. To address the challenges arising from the scarcity of samples, a sample identification framework based on IoT communication characteristics is devised to create synthetic samples from the raw sequence samples, enabling concurrent processing of the raw and synthetic samples using an identification model featuring two separate sequence networks. Comparative assessments of our method against baseline sequence models and the latest techniques demonstrate the advantages of our approach in identifying non-IP heterogeneous IoT traffic. The experimental results indicate that our method achieves an average accuracy improvement of 29.7% compared to baseline models using only raw samples. Furthermore, our method shows improvements of 22.1%, 21.5%, and 21.8% in macro precision, macro recall, and macro F1-score, respectively, over the latest method.

In this study, a fortified Daqu (FF Daqu) was prepared using high cellulase-producing Bacillus subtilis, and the effects of in situ fortification on the physicochemical properties, flavor, active microbial community and metabolism of Daqu were analyzed. The saccharification power, liquefaction power, and cellulase activity of the FF Daqu were significantly increased compared with that of the traditional Daqu (CT Daqu). The overall differences in flavor components and their contents were not significant, but the higher alcohols were lower in FF Daqu. The relative abundance of dominant active species in FF Daqu was 85.08% of the total active microbiota higher than 63.42% in CT Daqu, and the biomarkers were Paecilomyces variotii and Aspergillus cristatus, respectively. The enzymes related to starch and sucrose metabolic pathways were up-regulated and expressed in FF Daqu. In the laboratory level simulation of baijiu brewing, the yield of baijiu was increased by 3.36% using FF Daqu.

Abstract Stitched fluorescence microscope images inevitably exist in various types of stripes or artifacts caused by uncertain factors such as optical devices or specimens, which severely affects the image quality and downstream quantitative analysis. Here, we present a deep learning-based Stripe Self-Correction method, so-called SSCOR. Specifically, we propose a proximity sampling scheme and adversarial reciprocal self-training paradigm that enable SSCOR to utilize stripe-free patches sampled from the stitched microscope image itself to correct their adjacent stripe patches. Comparing to off-the-shelf approaches, SSCOR can not only adaptively correct non-uniform, oblique, and grid stripes, but also remove scanning, bubble, and out-of-focus artifacts, achieving the state-of-the-art performance across different imaging conditions and modalities. Moreover, SSCOR does not require any physical parameter estimation, patch-wise manual annotation, or raw stitched information in the correction process. This provides an intelligent prior-free image restoration solution for microscopists or even microscope companies, thus ensuring more precise biomedical applications for researchers.

Abstract Objective The purpose of this study was to investigate the associations between mobile phone dependency, bedtime procrastination, FoMO, and sleep quality among college students during the COVID-19 outbreak. Specifically, we examined whether bedtime procrastination and FoMO mediate the relationship between mobile phone dependency and sleep quality. Methods A total of 881 college students completed an online survey in May 2022 in Shanghai, China. Mobile Phone Involvement Questionnaire, Bedtime Procrastination Scale, Fear of Missing Out Scale and Pittsburgh Sleep Quality Index were used to assess mobile phone dependency, bedtime procrastination, fear of missing out, and sleep quality, respectively. Multiple linear regression and mediation analysis were conducted. Results The correlation analyses indicated mobile phone dependency was positively associated with fear of missing out, bedtime procrastination, and poor sleep quality among college students. The structural equation modeling analyses revealed that mobile phone dependency had significant indirect effects on sleep quality through bedtime procrastination (indirect effect: 0.030, 95%CI: 0.022–0.041) and fear of missing out (indirect effect: 0.013, 95%CI: 0.003–0.023). Conclusion The findings indicated that bedtime procrastination and fear of missing out are mediators mediating the relationship between mobile phone dependency with sleep quality. Bedtime procrastination and fear of missing out should be considered as potential intervention targets for reducing mobile phone dependency and improving sleep quality in college students.

Background Daqu is an essential starter for baijiu brewing in China. However, the microbial enrichment and metabolic characteristics of Daqu formed at different fermentation temperatures are still unclear. Methods High-throughput sequencing technology and the non-targeted metabolomics were used to compare the microbial communities and metabolites of Taorong-type high-temperature Daqu and middle-temperature Daqu. In this study, the relationship between microorganisms and metabolites was established. Results The study found that the composition and metabolites of the microbial community differed due to the difference in Daqu-making temperature. The bacterial diversity of Taorong-type high-temperature Daqu was higher than that of middle-temperature Daqu, while the fungal community diversity of Taorong-type middle-temperature Daqu was higher than that of high temperature Daqu. A total of 1,034 differential metabolites were screened from the two types of Daqu, and 76 metabolites with significant differences were detected (P < 0.001 and variable importance in projection (VIP) > 1.15). Tetraacetylethylenediamine is the metabolite with the largest differential fold among the 76 differential metabolites, which can be used as a potential marker metabolite of high-temperature Daqu. Conclusion This study helps elucidate the microbial assembly mechanisms and functional expression under different processing conditions through a further understanding of the composition and metabolic profile differences of different types of Daqu microflora in Taorong-type baijiu.

Agriculture is the primary source of human survival, which provides the most basic living and survival conditions for human beings. As living standards continue to improve, people are also paying more attention to the quality and safety of agricultural products. Therefore, the detection of agricultural product quality is very necessary. In the past decades, the spectroscopy technique has been widely used because of its excellent results in agricultural quality detection. However, traditional spectral inspection methods cannot accurately describe the internal information of agricultural products. With the continuous research and development of optical properties, it has been found that the internal quality of an object can be better reflected by separating the properties of light, such as its absorption and scattering properties. In recent years, spatially resolved spectroscopy has been increasingly used in the field of agricultural product inspection due to its simple compositional structure, low-value cost, ease of operation, efficient detection speed, and outstanding ability to obtain information about agricultural products at different depths. It can also separate optical properties based on the transmission equation of optics, which allows for more accurate detection of the internal quality of agricultural products. This review focuses on the principles of spatially resolved spectroscopy, detection equipment, analytical methods, and specific applications in agricultural quality detection. Additionally, the optical properties methods and direct analysis methods of spatially resolved spectroscopy analysis methods are also reported in this paper.

AimOur study aimed to construct a practical risk prediction model for metabolic syndrome (MetS) based on the longitudinal health check-up data, considering both the baseline level of physical examination indicators and their annual average cumulative exposure, and to provide some theoretical basis for the health management of Mets.MethodsThe prediction model was constructed in male and female cohorts, separately. The shared set of predictive variables screened out from 49 important physical examination indicators by the univariate Cox model, Lasso-Cox model and the RSF algorithm collectively was further screened by Cox stepwise regression method. The screened predictors were used to construct prediction model by the Cox proportional hazards regression model and RSF model, respectively. Subsequently, the better method would be selected to develop final MetS predictive model according to comprehensive comparison and evaluation. Finally, the optimal model was validated internally and externally by the time-dependent ROC curve (tdROC) and concordance indexes (C-indexes). The constructed predictive model was converted to a web-based prediction calculator using the “shiny” package of the R4.2.1 software.ResultsA total of 15 predictors were screened in the male cohort and 9 predictors in the female cohort. In both male and female cohorts, the prediction error curve of the RSF model was consistently lower than that of the Cox proportional hazards regression model, and the integrated Brier score (IBS) of the RSF model was smaller, therefore, the RSF model was used to develop the final prediction model. Internal validation of the RSF model showed that the area under the curve (AUC) of tdROC for 1 year, 3 years and 5 years in the male cohort were 0.979, 0.991, and 0.983, and AUCs in the female cohort were 0.959, 0.975, and 0.978, respectively, the C-indexes calculated by 500 bootstraps of the male and female cohort RSF models are above 0.7. The external validation also showed that the model has good predictive ability.ConclusionThe risk predictive model for MetS constructed by RSF in this study is more stable and reliable than Cox proportional hazards regression model, and the model based on multiple screening of routine physical examination indicators has performed well in both internal and external data, and has certain clinical application value.

This study investigated the effects of a dance-based exergaming on Chinese college students’ energy expenditure, self-efficacy, and enjoyment in comparison with the traditional aerobic dance exercise. Forty young adults (33 females; Mage = 21.55 years, SD = 2.06) completed two separate 20 min exercise sessions with 10 min intervals on the same day: (1) Xbox 360 Kinect Just Dance exergaming session; and (2) a traditional instructor-led aerobic dance exercise session. Participants’ energy expenditure (Kcal/session) was measured by the ActiGraph GT9X Link accelerometers, and their perceived self-efficacy and enjoyment were assessed via validated surveys following each session. Dependent t-test indicated significant differences in participants’ enjoyment (t = −1.83, p = 0.04). Specifically, participants in the dance-based exergaming session reported a higher level of enjoyment (M = 3.96, SD = 0.65) as compared to the aerobic dance session (M = 3.61, SD = 0.54). However, there was no significant difference in energy expenditure and self-efficacy between the two sessions. Findings suggest that college students had comparable energy expenditure as the traditional aerobic dance session while experiencing more fun and enjoyment. This suggests that exergaming can be a fun exercise alternative for promoting physical activity among young adults.

This paper introduces a method for high-resolution lattice image reconstruction and dislocation analysis based on diffraction extinction. The approach primarily involves locating extinction spots in the Fourier transform spectrum (reciprocal space) and constructing corresponding diffraction wave functions. By the coherent combination of diffraction and transmission waves, the lattice image of the extinction planes is reconstructed. This lattice image is then used for dislocation localization, enabling the observation and analysis of crystal planes that exhibit electron diffraction extinction effects and atomic jump arrangements during high-resolution transmission electron microscopy (HRTEM) characterization. Furthermore, due to the method’s effectiveness in localizing dislocations, it offers a unique advantage when analyzing high-resolution images with relatively poor quality. The feasibility of this method is theoretically demonstrated in this paper. Additionally, the method was successfully applied to observed edge dislocations, such as 1/6[211−], 1/6[2−11−], and 1/2[01−1], which are not easily observable in conventional HRTEM characterization processes, in electro-deposited Cu thin films. The Burgers vectors were determined. Moreover, this paper also attempted to observe screw dislocations that are challenging to observe in high-resolution transmission electron microscopy. By shifting a pair of diffraction extinction spots and superimposing the reconstructed images before and after the shift, screw dislocations with a Burgers vector of 1/2[011−] were successfully observed in electro-deposited Cu thin films.

Background: Public health guidelines have called for innovative and flexible physical activity (PA) intervention strategies to promote PA and health amid the coronavirus disease 2019 (COVID-19) pandemic. Therefore, this study's purpose was to examine the effects of a home-based, YouTube-delivered PA intervention grounded in self-determination theory on young adults’ free-living PA, sedentary behavior, and sleep quality (NCT04499547). Methods: Sixty-four young adults (48 females; age = 22.8 ± 3.4 years, mean ± SD; body mass index = 23.1 ± 2.6 kg/m2) were randomized (1:1) into the intervention group, which received weekly aerobic and muscle-strengthening PA videos, or control group, which received weekly general health education videos, for 12 weeks. Our primary outcome was free-living moderate-to-vigorous PA (MVPA) and our secondary outcomes were sedentary behavior, light PA, and sleep quality (measured using ActiGraph accelerometers) along with muscle-strengthening PA frequency, self-determination theory-related motivation (non-regulation, external regulation, introjected regulation, identified regulation, integrated regulation, and intrinsic regulation), and perceived PA barriers (assessed using validated questionnaires). Repeated measures analysis of variances (ANOVAs) examined between-group differences at an adjusted significance level of 0.004 and effect sizes as partial eta-squared (ηp2). Results: We observed statistically significant interaction effects for MVPA, sleep efficiency, muscle-strengthening PA frequency, non-regulation, integrated regulation, intrinsic regulation, and perceived PA barriers (F(1, 62) = 10.75–77.67, p < 0.001–0.002, ηp2 = 0.15–0.56) with all outcomes favoring the intervention group. We observed no statistically significant differences in either group for sedentary behavior, light PA, sleep duration, or external, introjected, and identified regulations after 12 weeks (F(1, 62) = 1.11–3.64, p = 0.06–0.61). Conclusion: With national COVID-19 restrictions still in place and uncertainty regarding post-pandemic PA environments and behaviors, a remote, YouTube-delivered PA intervention may help foster clinically meaningful improvements in young adults’ free-living MVPA, muscle-strengthening PA frequency, sleep efficiency, PA-related intrinsic motivation, and perceived PA barriers.

Due to the profitability and selfishness of crowdfunding system users, under fixed budget conditions, there are problems, such as low task completion rate due to insufficient participants and low data quality. However, the existing incentive mechanisms are mainly based on traditional economics, which believes that whether users participate in tasks depends on whether the benefits of the task outweigh the costs. Behavioral economics shows that people judge the value of gains and losses according to a reference point. The weight given to losses is more important than the weight given to the same gains. Therefore, this article considers the impact of reference dependency and loss aversion on user decision-making and proposes a participant selection mechanism based on reference dependency (PSM-RD) and a quality assurance mechanism based on loss aversion (QAM-LA). PSM-RD uses reference points to influence user pricing and selects more participants based on relative value. QAM-LA pays additional rewards based on the data quality of participants and motivates them to improve data quality by reconstructing utility functions. The simulation results show that compared with the ABSee mechanism, data quality has improved by 17%, and the value of completed tasks has increased by at least 40%.

Many studies have explored the efficacy of probiotics on autism spectrum disorder (ASD) in children, but there is no consensus on the curative effect. This systematic review and meta-analysis aimed to comprehensively investigate whether probiotics could improve behavioral symptoms in children with ASD. A systematic database search was conducted and a total of seven studies were included in the meta-analysis. We found a nonsignificant overall effect size of probiotics on behavioral symptoms in children with ASD (SMD = −0.24, 95% CI: −0.60 to 0.11, p = 0.18). However, a significant overall effect size was found in the subgroup of the probiotic blend (SMD = −0.42, 95% CI: −0.83 to −0.02, p = 0.04). Additionally, these studies provided limited evidence for the efficacy of probiotics due to their small sample sizes, a shorter intervention duration, different probiotics used, different scales used, and poor research quality. Thus, randomized, double-blind, and placebo-controlled studies following strict trial guidelines are needed to precisely demonstrate the therapeutic effects of probiotics on ASD in children.

Saikosaponins comprise a large group of chemical components present in the Bupleurum species that have attracted attention in the field of medicine because of their significant biological activities. Due to the high polarity, structural similarity, and the presence of several isomers of this class of components, their structural identification is extremely challenging. In this study, the mass spectrometric fragmentation pathways, UV spectral features, and chromatographic behavior of different types of saikosaponins were investigated using 24 standard substances. Saikosaponins containing carbonyl groups (C=O) in the aglycone produced fragment ions by loss of 30 Da, and in addition, type IV saikosaponins could produce [aglycone−CH2OH−OH−H]− and [aglycone−H2O−H]− fragment ions through neutral losses at positions C16 and C17. The above characteristic ions can be used to identify saikosaponins. More notably, the identification process of saikosaponins was systematically summarized, and using this method, 109 saikosaponins were identified or tentatively characterized from the saikosaponins extract of Bupleurum marginatum var. stenophyllum (BMS) using UPLC-PDA-Q/TOF-MS with both data-dependent acquisition (DDA) and data-independent acquisition (DIA) modes, of which 25 were new compounds and 60 were first discovered from BMS. Further studies revealed that the saikosaponins profiles of BMS, Bupleurum chinense DC (BC), and Bupleurum marginatum Wall. ex DC (BMW) were very similar. This work is of great significance for the basic research of the Bupleurum species and provides strong technical support to solve the resource problems associated with Radix Bupleuri.

In this paper, we propose a novel deep convolutional neural network (DCNN) for removing snowflakes from light field (LF) images. We observe that snowflakes in LF images always interrupt slopes in background scenes in epipolar plane images (EPIs), which means that snowflakes may be easily detected in EPIs. Our method takes 3D EPI volumes (i.e., stacked subaperture views along the same row or column of an LF image) as input. In this way, our snowflake detector based on a 3D residual network with a convolutional long short-term memory (ResNet-ConvLSTM) can utilize both contextual information and 3D scene structural information to effectively detect snowflakes of different sizes in LF images. Then, an encoder-decoder-based LF image restoration network is proposed to restore the background image. Finally, extensive experiments for comparison with the state-of-the-art methods demonstrate the effectiveness of our method for challenging scenes.

In this paper, we study the problem of low-rank tensor completion with the purpose of recovering a low-rank tensor from a tensor with partial observed items. To date, there are several different definitions of tensor ranks. We focus the study on the low tubal rank tensor completion task. Previous works solve the low tubal rank tensor completion/recovery problems by convex tensor nuclear norm minimization. However, this kind of tensor nuclear norm is orientation dependent, which is originally due to the definition of tensor-tensor product. Based on the convex tensor nuclear norm minimization, the tensor recovery performance varies when the orientation of the input data is different. However, in practice, it is generally hard to choose the best way of the data input. To address this issue, we propose a new convex model which is based on the sum of tensor nuclear norm minimization. It includes the existing tensor nuclear norm minimization model as a special case which is corresponding to an orientation of the input data. The proposed model is convex and thus can be solved efficiently. Numerical experiments on images and video sequences demonstrate the effectiveness of our proposed method.

Polygonum multiflorum Thunb. and its processed products have been used in China for centuries due to their multiple beneficial effects to human body. Currently, liver injuries caused by taking P. multiflorum have been reported worldwide, but the potential toxic components and possible mechanism that caused hepatotoxicity remain unclear. It is worth noting that the processing procedure could significantly decrease the toxicity of raw P. multiflorum and the processed products of P. multiflorum are considered to be relatively safe. However, the processing mechanism is still ambiguous, and there is the lack of a scientific approach to control the quality of P. multiflorum praeparata. This study is the first review that summarizes the recently advances (from 2007 to 2017) in the chemical analysis of P. multiflorum, and provides comprehensive information on the quantitative and qualitative analysis of P. multiflorum as well as its related species. In addition, the processing mechanism and quality evaluation of processed P. multiflorum are discussed. Moreover, the toxicity of P. multiflorum is analyzed from the perspectives of exploration of the proposed toxic ingredients, metabolite identification, metabolomics studies, and exogenous contaminant determination. Furthermore, trends and perspectives for future research of this medicine are discussed.