Examining the Gateway Hypothesis and Mapping Substance Use Pathways on Social Media: Machine Learning Approach.

BACKGROUND: Substance misuse presents significant global public health challenges. Understanding transitions between substance types and the timing of shifts to polysubstance use is vital to developing effective prevention and recovery strategies. The gateway hypothesis suggests that high-risk substance use is preceded by lower-risk substance use. However, the source of this correlation is hotly contested. While some claim that low-risk substance use causes subsequent, riskier substance use, most people using low-risk substances also do not escalate to higher-risk substances. Social media data hold the potential to shed light on the factors contributing to substance use transitions. OBJECTIVE: By leveraging social media data, our study aimed to gain a better understanding of substance use pathways. By identifying and analyzing the transitions of individuals between different risk levels of substance use, our goal was to find specific linguistic cues in individuals' social media posts that could indicate escalating or de-escalating patterns in substance use. METHODS: We conducted a large-scale analysis using data from Reddit, collected between 2015 and 2019, consisting of over 2.29 million posts and approximately 29.37 million comments by around 1.4 million users from subreddits. These data, derived from substance use subreddits, facilitated the creation of a risk transition data set reflecting the substance use behaviors of over 1.4 million users. We deployed deep learning and machine learning techniques to predict the escalation or de-escalation transitions in risk levels, based on initial transition phases documented in posts and comments. We conducted a linguistic analysis to analyze the language patterns associated with transitions in substance use, emphasizing the role of n-gram features in predicting future risk trajectories. RESULTS: Our results showed promise in predicting the escalation or de-escalation transition in risk levels, based on the historical data of Reddit users created on initial transition phases among drug-related subreddits, with an accuracy of 78.48% and an F1-score of 79.20%. We highlighted the vital predictive features, such as specific substance names and tools indicative of future risk escalations. Our linguistic analysis showed that terms linked with harm reduction strategies were instrumental in signaling de-escalation, whereas descriptors of frequent substance use were characteristic of escalating transitions. CONCLUSIONS: This study sheds light on the complexities surrounding the gateway hypothesis of substance use through an examination of web-based behavior on Reddit. While certain findings validate the hypothesis, indicating a progression from lower-risk substances such as marijuana to higher-risk ones, a significant number of individuals did not show this transition. The research underscores the potential of using machine learning with social media analysis to predict substance use transitions. Our results point toward future directions for leveraging social media data in substance use research, underlining the importance of continued exploration before suggesting direct implications for interventions.

Predicting academic achievement from the collaborative influences of executive function, physical fitness, and demographic factors among primary school students in China: ensemble learning methods.

BACKGROUND: Elevated levels of executive function and physical fitness play a pivotal role in shaping future quality of life. However, few studies have examined the collaborative influences of physical and mental health on academic achievement. This study aims to investigate the key factors that collaboratively influence primary school students' academic achievement from executive function, physical fitness, and demographic factors. Additionally, ensemble learning methods are employed to predict academic achievement, and their predictive performance is compared with individual learners. METHODS: A cluster sampling method was utilized to select 353 primary school students from Huai'an, China, who underwent assessments for executive function, physical fitness, and academic achievement. The recursive feature elimination cross-validation method was employed to identify key factors that collaboratively influence academic achievement. Ensemble learning models, utilizing eXtreme Gradient Boosting and Random Forest algorithms, were constructed based on Bagging and Boosting methods. Individual learners were developed using Support Vector Machine, Decision Tree, Logistic Regression, and Linear Discriminant Analysis algorithms, followed by the establishment of a Stacking ensemble learning model. RESULTS: Our findings revealed that sex, body mass index, muscle strength, cardiorespiratory function, inhibition, working memory, and shifting were key factors influencing the academic achievement of primary school students. Moreover, ensemble learning models demonstrated superior predictive performance compared to individual learners in predicting academic achievement among primary school students. CONCLUSIONS: Our results suggest that recognizing sex differences and emphasizing the simultaneous development of cognition and physical well-being can positively impact the academic development of primary school students. Ensemble learning methods warrant further attention, as they enable the establishment of an accurate academic early warning system for primary school students.

Contrastive Transformer Hashing for Compact Video Representation.

Video hashing learns compact representation by mapping video into low-dimensional Hamming space and has achieved promising performance in large-scale video retrieval. It is challenging to effectively exploit temporal and spatial structure in an unsupervised setting. To fulfill this gap, this paper proposes Contrastive Transformer Hashing (CTH) for effective video retrieval. Specifically, CTH develops a bidirectional transformer autoencoder, based on which visual reconstruction loss is proposed. CTH is more powerful to capture bidirectional correlations among frames than conventional unidirectional models. In addition, CTH devises multi-modality contrastive loss to reveal intrinsic structure among videos. CTH constructs inter-modality and intra-modality triplet sets and proposes multi-modality contrastive loss to exploit inter-modality and intra-modality similarities simultaneously. We perform video retrieval tasks on four benchmark datasets, i.e., UCF101, HMDB51, SVW30, FCVID using the learned compact hash representation, and extensive empirical results demonstrate the proposed CTH outperforms several state-of-the-art video hashing methods.

Heme Oxygenase-1 Is Involved in the Repair of Oxidative Damage Induced by Oxidized Fish Oil in Litopenaeus vannamei by Sulforaphane.

Heme oxygenase-1 (HO-1), which could be highly induced under the stimulation of oxidative stress, functions in reducing the damage caused by oxidative stress, and sulforaphane (SFN) is an antioxidant. This study aims to investigate whether HO-1 is involved in the repair of oxidative damage induced by oxidized fish oil (OFO) in Litopenaeus vannamei by sulforaphane (SFN). The oxidative stress model of L. vannamei was established by feeding OFO feed (OFO accounts for 6%), and they were divided into the following four groups: control group (injected with dsRNA-EGFP and fed with common feed), dsRNA-HO-1 group (dsRNA-HO-1, common feed), dsRNA-HO-1 + SFN group (dsRNA-HO-1, supplement 50 mg kg-1 SFN feed), and SFN group (dsRNA-EGFP, supplement 50 mg kg-1 SFN feed). The results showed that the expression level of HO-1 in the dsRNA-HO-1 + SFN group was significantly increased compared with the dsRNA-HO-1 group (p < 0.05). The activities of SOD in muscle and GPX in hepatopancreas and serum of the dsRNA-HO-1 group were significantly lower than those of the control group, and MDA content in the dsRNA-HO-1 group was the highest among the four groups. However, SFN treatment increased the activities of GPX and SOD in hepatopancreas, muscle, and serum and significantly reduced the content of MDA (p < 0.05). SFN activated HO-1, upregulated the expression of antioxidant-related genes (CAT, SOD, GST, GPX, Trx, HIF-1α, Nrf2, prx 2, Hsp 70), and autophagy genes (ATG 3, ATG 5), and stabilized the expression of apoptosis genes (caspase 2, caspase 3) in the hepatopancreas (p < 0.05). In addition, knocking down HO-1 aggravated the vacuolation of hepatopancreas and increased the apoptosis of hepatopancreas, while the supplement of SFN could repair the vacuolation of hepatopancreas and reduce the apoptosis signal. In summary, HO-1 is involved in the repair of the oxidative damage induced by OFO in L. vannamei by SFN.

Physics-informed neural networks to solve lumped kinetic model for chromatography process.

Numerical method is widely used for solving the mechanistic models of chromatography process, but it is time-consuming and hard to response in real-time. Physics-informed neural network (PINN) as an emerging technology combines the structure of neural network with physics laws, and is getting noticed for solving physics problems with a balanced accuracy and calculation speed. In this research, a proof-of-concept study was carried out to apply PINN to chromatography process simulation. The PINN model structure was designed for the lumped kinetic model (LKM) with all LKM parameters. The PINN structure, training data and model complexity were optimized, and an optimal mode was obtained by adopting an in-series structure with a nonuniform training data set focusing on the breakthrough transition region. A PINN for LKM (LKM-PINN) consisting of four neural networks, 12 layers and 606 neurons was then used for the simulation of breakthrough curves of chromatography processes. The LKM parameters were estimated with two breakthrough curves and used to infer the breakthrough curves at different residence times, loading concentrations and column sizes. The results were comparable to that obtained with numerical methods. With the same raw data and constraints, the average fitting error for LKM-PINN model was 0.075, which was 0.081 for numerical method. With the same initial guess, the LKM-PINN model took 160 s to complete the fitting, while the numerical method took 7 to 72 min, depending on the fitting settings. The fitting speed of LKM-PINN model was further improved to 30 s with random initial guess. Thus, the LKM-PINN model developed in this study is capable to be applied to real-time simulation for digital twin.

Supervised machine learning: A new method to predict the outcomes following exercise intervention in children with autism spectrum disorder.

The individual differences among children with autism spectrum disorder (ASD) may make it challenging to achieve comparable benefits from a specific exercise intervention program. A new method for predicting the possible outcomes and maximizing the benefits of exercise intervention for children with ASD needs further exploration. Using the mini-basketball training program (MBTP) studies to improve the symptom performance of children with ASD as an example, we used the supervised machine learning method to predict the possible intervention outcomes based on the individual differences of children with ASD, investigated and validated the efficacy of this method. In a long-term study, we included 41 ASD children who received the MBTP. Before the intervention, we collected their clinical information, behavioral factors, and brain structural indicators as candidate factors. To perform the regression and classification tasks, the random forest algorithm from the supervised machine learning method was selected, and the cross validation method was used to determine the reliability of the prediction results. The regression task was used to predict the social communication impairment outcome following the MBTP in children with ASD, and explainable variance was used to evaluate the predictive performance. The classification task was used to distinguish the core symptom outcome groups of ASD children, and predictive performance was assessed based on accuracy. We discovered that random forest models could predict the outcome of social communication impairment (average explained variance was 30.58%) and core symptom (average accuracy was 66.12%) following the MBTP, confirming that the supervised machine learning method can predict exercise intervention outcomes for children with ASD. Our findings provide a novel and reliable method for identifying ASD children most likely to benefit from a specific exercise intervention program in advance and a solid foundation for establishing a personalized exercise intervention program recommendation system for ASD children.

A novel C-type lectin for Litopenaeus vannamei involved in the innate immune response against Vibrio infection.

C-type lectins (CTLs), as a member of pattern recognition receptors, play a vital role in the innate immune response of invertebrates to eliminate micro-invaders. In this study, a novel CTL of Litopenaeus vannamei, namely, LvCTL7, was successfully cloned, with an open reading frame of 501 bp and a capability to encode 166 amino acids. Blast analysis showed that the amino acid sequence similarity between LvCTL7 and MjCTL7 (Marsupenaeus japonicus) was 57.14%. LvCTL7 was mainly expressed in hepatopancreas, muscle, gill and eyestalk. Vibrio harveyi can significantly affect LvCTL7 expression level in hepatopancreases, gills, intestines and muscles (p < 0.05). LvCTL7 recombinant protein can bind to Gram-positive bacteria (Bacillus subtilis) and Gram-negative bacteria (Vibrio parahaemolyticus and V. harveyi). It can cause the agglutination of V. alginolyticus and V. harveyi, but it had no effect on Streptococcus agalactiae and B. subtilis. The expression levels of SOD, CAT, HSP 70, Toll 2, IMD and ALF genes in the challenge group added with LvCTL7 protein were more stable than those in the direct challenge group (p < 0.05). Moreover, knockdown of LvCTL7 by double-stranded RNA interference downregulated the expression levels of genes (ALF, IMD and LvCTL5) that protect against bacterial infection (p < 0.05). These results indicated that LvCTL7 had microbial agglutination and immunoregulatory activity, and it was involved in the innate immune response against Vibrio infection in L. vannamei.

Involvement of Nrf2 in the immune regulation of Litopenaeus vannamei against Vibrio harveyi infection.

NF-E2-related factor-like-2 (Nrf2) is a transcription factor that belongs to the Cap'n'Collar transcription factor family and plays a role in regulating inflammation, autophagy, metabolism, proteostasis, and cancer prevention. However, its influence on Vibrio spp infection in L. vannamei remains uncertain. In this study, the effects of Nrf2 on the immune response in Vibrio spp infection was determined by RT-PCR and histopathological analysis. The results showed that RNAi of Nrf2 significantly decreased the expression of antioxidant-related genes (CAT, SOD and GST; p < 0.05), and significantly up-regulated inflammation-related genes (IMD, pro-PO, P38, Toll, Hsp70, NFκB and RAB6A; p < 0.05) and the apoptosis gene (caspase3). Under the infection of V. harveyi, histopathological analysis showed that after RNAi of Nrf2, the hepatopancreas of shrimp has an abnormal arrangement of hepatic tubules and vacuolization of hepatocyte; The basement membrane is peeled off and the epithelial cells are massively necrotic. Compared with the RNAi of Nrf2 group, the tissue damage in the SFN group was much lessened, and there were fewer apoptosis signals in the TUNEL assay. In conclusion, this experiment indicated that Nrf2 is involved in the regulation of inflammatory response, oxidative stress,and apoptosis induced by V. harveyi in L. vannamei.

Effect of sub-lethal ammonia and nitrite stress on autophagy and apoptosis in hepatopancreas of Pacific whiteleg shrimp Litopenaeusvannamei.

Oxidative stress caused by ammonia and nitrite, affect the health and growth of aquaculture animals, results in oxidative damages. However, the toxic mechanism and pathogenesis of ammonia and nitrite to aquatic invertebrates are not completely clear. The present study was conducted to investigate the effects of sub-lethal ammonia and nitrite on autophagy and apoptosis in hepatopancreas of Pacific whiteleg shrimp Litopenaeus vannamei. Shrimps were exposed to sub-lethal ammonia (20 mg/L) and nitrite (20 mg/L) for 72 h, respectively. Hepatopancreas was collected for investigating the autophagy and apoptosis under stress conditions. The results showed that ammonia stress could induce up-regulated of autophagy (ATG3, ATG4, ATG10 and ATG12) and apoptosis (Caspase3 and P53) genes transcription. Nitrite stress could also induce up-regulated of autophagy (ATG3, ATG4, ATG5 and ATG10) and apoptosis (Caspase3) genes transcription. The expression of the autophagy related genes increased at first and then decreased with increasing exposure time. The atrophy, lysis, vacuolation of cell and other tissue damages in hepatopancreas were observed after 72h exposure to ammonia and nitrite. The results indicated that ammonia and nitrite stress could induce autophagy and apoptosis, and results in oxidative damage.

A class-specific mean vector-based weighted competitive and collaborative representation method for classification.

Collaborative representation-based classification (CRC), as a typical kind of linear representation-based classification, has attracted more attention due to the effective and efficient pattern classification performance. However, the existing class-specific representations are not competitively learned from collaborative representation for achieving more informative pattern discrimination among all the classes. With the purpose of enhancing the power of competitive and discriminant representations among all the classes for favorable classification, we propose a novel CRC method called the class-specific mean vector-based weighted competitive and collaborative representation (CMWCCR). The CMWCCR mainly contains three discriminative constraints including the competitive, mean vector and weighted constraints that fully employ the discrimination information in different ways. In the competitive constraint, the representations from any one class and the other classes are adapted for learning competitive representations among all the classes. In the newly designed mean vector constraint, the mean vectors of all the class-specific training samples with the corresponding class-specific representations are taken into account to further enhance the competitive representations. In the devised weighted constraint, the class-specific weights are constrained on the representation coefficients to make the similar classes have more representation contributions to strengthening the discrimination among all the class-specific representations. Thus, these three constraints in the unified CMWCCR model can complement each other for competitively learning the discriminative class-specific representations. To verify the CMWCCR classification performance, the extensive experiments are conducted on twenty-eight data sets in comparisons with the state-of-the-art representation-based classification methods. The experimental results show that the proposed CMWCCR is an effective and robust CRC method with satisfactory performance.

Silencing of Nrf2 in Litopenaeus vannamei, decreased the antioxidant capacity, and increased apoptosis and autophagy.

Nuclear factor E2-related factor 2 (Nrf2) is a multifunctional transcription factor that plays an important role in antioxidant activities. However, its effect on antioxidant capacity in Litopenaeus vannamei, an economically important crustacean, remains unclear. In this study, the role of Nrf2 in response to oxidative stress in L. vannamei was determined by its effect on relevant gene expression and enzymatic activity. Nrf2 was cloned and analyzed. Results revealed that Nrf2 contains a 1575 bp open reading frame encoding 524 amino acids and a conserved bZIP Maf domain. The sequence similarity of Nrf2 between L. vannamei and Homarus americanus is 81%. Although the Nrf2 expression was detected in all tissues, the Nrf2 expression levels were the highest in the hepatopancreas, followed by the eyestalk and muscle. RNA interference significantly decreased the expression of antioxidant-related genes (SOD, GPX, CAT, Trx, and HO-1; p < 0.05), significantly upregulated the expression of autophagy genes (Atg3, Atg4, Atg5, Atg10, and Atg12; p < 0.05) and apoptosis genes (Caspase-3 and P53; p < 0.05). Moreover, SOD, CAT, and GPX enzyme activities decreased whereas the MDA activity increased. The histological results of the shrimp injected with dsRNA-Nrf2 showed that the hepatic tubules were irregularly arranged, the lumen was abnormal, and a few hepatic tubules were significantly enlarged compared with those of the dsRNA-EGFP group. The hepatocytes were also vacuolated. In conclusion, this study provided evidence that Nrf2 is involved in the regulation of antioxidant capacity, oxidative stress, apoptosis, and autophagy in shrimp.

Weighted discriminative collaborative competitive representation for robust image classification.

Collaborative representation-based classification (CRC) is a famous representation-based classification method in pattern recognition. Recently, many variants of CRC have been designed for many classification tasks with the good classification performance. However, most of them ignore the inter-class pattern discrimination among the class-specific representations, which is very critical for strengthening the pattern discrimination of collaborative representation (CR). In this article, we propose a novel CR approach for image classification, called weighted discriminative collaborative competitive representation (WDCCR). The proposed WDCCR designs the discriminative and competitive collaborative representation among all the classes by fully considering the class information. On the one hand, we incorporate two discriminative constraints into the unified WDCCR model. Both constraints are the competitive class-specific representation residuals and the pairs of class-specific representations for each query sample. On the other hand, the constraint of the weighted categorical representation coefficients is introduced into the proposed model for further enhancing the power of discriminative and competitive representation. In the weighted constraint, we assume that the different classes of each query sample should have less contribution to the representation with the small representation coefficients, and then two types of weight factors are designed to constrain the representation coefficients. Furthermore, the robust WDCCR (R-WDCCR) is proposed with l1-norm representation fidelity for recognizing noisy images. Extensive experiments on six image data sets demonstrate the effective and robust superiorities of the proposed WDCCR and R-WDCCR over the related state-of-the-art representation-based classification methods.

Quaternion wavelet transform based full reference image quality assessment for multiply distorted images.

Most of real-world image distortions are multiply distortion rather than single distortion. To address this issue, in this paper we propose a quaternion wavelet transform (QWT) based full reference image quality assessment (FR IQA) metric for multiply distorted images, which jointly considers the local similarity of phase and magnitude of each subband via QWT. Firstly, the reference images and distorted images are decomposed by QWT, and then the similarity of amplitude and phase are calculated on each subband, thirdly the IQA metric is constructed by the weighting method considering human visual system (HVS) characteristics, and lastly the scores of each subband are averaged to get the quality score of test image. Experimental results show that the proposed method outperforms the state of art in multiply distorted IQA.

Semi-Paired Discrete Hashing: Learning Latent Hash Codes for Semi-Paired Cross-View Retrieval.

Due to the significant reduction in computational cost and storage, hashing techniques have gained increasing interests in facilitating large-scale cross-view retrieval tasks. Most cross-view hashing methods are developed by assuming that data from different views are well paired, e.g., text-image pairs. In real-world applications, however, this fully-paired multiview setting may not be practical. The more practical yet challenging semi-paired cross-view retrieval problem, where pairwise correspondences are only partially provided, has less been studied. In this paper, we propose an unsupervised hashing method for semi-paired cross-view retrieval, dubbed semi-paired discrete hashing (SPDH). In specific, SPDH explores the underlying structure of the constructed common latent subspace, where both paired and unpaired samples are well aligned. To effectively preserve the similarities of semi-paired data in the latent subspace, we construct the cross-view similarity graph with the help of anchor data pairs. SPDH jointly learns the latent features and hash codes with a factorization-based coding scheme. For the formulated objective function, we devise an efficient alternating optimization algorithm, where the key binary code learning problem is solved in a bit-by-bit manner with each bit generated with a closed-form solution. The proposed method is extensively evaluated on four benchmark datasets with both fully-paired and semi-paired settings and the results demonstrate the superiority of SPDH over several other state-of-the-art methods in term of both accuracy and scalability.