Search

Abstract In recommender systems, leveraging auxiliary behaviors (e.g. view, cart) to enhance the recommendation in the target behavior (e.g. purchase) is crucial for mitigating the sparsity issue inherent in single-behavior recommendation. This has given rise to the multi-behavior recommendation (MBR). Existing MBR task faces two primary challenges. First, the irrelevant auxiliary behaviors that do not align with the target behavior, can negatively impact the prediction accuracy for user preference in the target behavior. Second, these methods typically learn coarse-grained user preferences, failing to model the consistency and distinctiveness among multiple behaviors at a fine-grained level. To address these issues, we propose a disentangled and denoised model for multi-behavior recommendation (DMR), which employs user preferences reflected in the target behavior to guide the learning of user and item embeddings in auxiliary behaviors. Specifically, we first design a disentangled graph convolutional network, modeling the fine-grained user preference under multiple behaviors in view of item attribute domains. We also propose a denoised contrastive learning strategy, where we align the user preferences in multiple behaviors by reducing the influence of noisy data existing in auxiliary behaviors. Experimental results on two real-world datasets show the proposal can improve the performance of MBR models effectively, which achieves on average 3.12% on the Retailrocket dataset and 3.28% on the Beibei dataset over the performance of state-of-the-art baselines. Extensive experiments also demonstrate our model’s competitive performance for fine-grained preference learning and denoised learning.

Abstract Condensed matter physics has often provided a platform for investigating the interplay between particles and fields in cases that have not been observed in high-energy physics. Here, using angle-resolved photoemission spectroscopy, we provide an example of this by visualizing the electronic structure of a noncentrosymmetric magnetic Weyl semimetal candidate NdAlSi in both the paramagnetic and ferrimagnetic states. We observe surface Fermi arcs and bulk Weyl fermion dispersion as well as the emergence of new Weyl fermions in the ferrimagnetic state. Our results establish NdAlSi as a magnetic Weyl semimetal and provide an experimental observation of ferrimagnetic regulation of Weyl fermions in condensed matter.

Background: Similar to the high-fat diet (HFD), the high-glycemic diet (HGD) contributes to the development and progression of type 2 diabetes mellitus (T2DM). However, the effect of HGD on gastrointestinal motility in T2DM and its u nderlying mechanisms remain unclear. Methods: Thirty C57BL/6J mice were randomly designated into the normal-feeding diet (NFD) group, HFD group, and HGD group. The plasma glucose, plasma insulin, and gastrointestinal motility were examined. Meanwhile, the tension of isolated colonic smooth muscle rings was calculated, and the gut microbiota was analyzed by 16s rDNA high-throughput sequencing. Result: After 16 weeks of HGD feeding, obesity, hyperglycemia, insulin resistance, and constipation were observed in HGD mice. Autonomic contraction frequency of the colonic neuromuscular system and electrical field stimulation-induced contractions were reduced in HGD mice. On the contrary, neuronal nitric oxide synthase activity and neuromuscular relaxation were found to be enhanced. Finally, gut microbiota analysis revealed that Rhodospirillaceae abundance significantly increased at the family level in HGD mice. At the genus level, the abundance of Insolitispirillum increased remarkably, whereas Turicibacter abundance decreased significantly in HGD mice. Conclusion: HGD induced constipation in obese diabetic mice, which we speculated that it may be related to neuromuscular dysmotility and intestinal microbiota dysbiosis.

Abstract Extracellular vesicles (EVs) and their clinical applications have been recognized as one of the most fast‐growing and promising fields. Numerous groups including us have expressed their concerns on the idea of isolating “pure” EVs for different purposes, as there are no specific markers to date that can examine the exact EV purity. Typically, major instrument need and EV yielding loss are primary compensations of getting EVs with higher purity. As such, especially in biomarker‐based clinical tests, ceaseless efforts for obtaining more pure EV are worth of discussion. We here propose alternative philosophy to tone down the importance of purity, but to emphasize the significance in clinical laboratory medicine to measure EVs obtained by well‐controlled isolation methods. As a result, EVs are enriched to the adequate degree to meet certain clinical applications such as disease diagnosis and/or prognosis. In this review, we will justify the adequate enrichment rationale with advances of EV definitions, markers and contaminants, EV isolation methods, as well as the conclusions acquired from clinical research using less pure but ease‐to‐operate EV isolation methods.

COVID-19 and the pandemic-induced lockdowns juxtaposed against the surge in the number of college graduates have made the dilemma of “fierce competition and difficult employment” more real. The employment of college students has become a topic of serious concern in society. This study aimed to develop a Future Employability Scale for Chinese college students and evaluate its reliability and validity. Based on the analysis of the literature, the study developed the initial measurement scale of the college students’ future employability and calibrated the initial measurement and question volume based on experts’ feedback. First, the students’ group was measured, and data from 389 university students were collected and analyzed. Second, the data collection and verification factor analysis of 387 university students were collected and verified, and the internal consistency reliability, split-half reliability, and validity of the scale were evaluated. Further, 68 college students were selected to evaluate their test-retest reliability after an interval of one month. The Future Employability Scale of college students had 28 items covering four dimensions: knowledge skill, personality quality, interpersonal network, and career development. The reliability test found that the total scale of the Future Employability Scale and the internal consistency reliability, split-half reliability, and retest reliability of each dimension were good, and the validity test suggested that the scale had good content validity, structural validity, and calibration correlation validity. With a clear structure, good reliability, and validity, the Future Employability Scale is a good tool to measure the future employability of college students.

Background and Purpose: Irritable bowel syndrome (IBS) is usually associated with chronic gastrointestinal disorders. Its most common subtype is accompanied with diarrhea (IBS-D). The enteric nervous system (ENS) modulates major gastrointestinal motility and functions whose aberration may induce IBS-D. The enteric neurons are susceptible to long-term neurotransmitter level alterations. The patchouli alcohol (PA), extracted from Pogostemonis Herba, has been reported to regulate neurotransmitter release in the ENS, while its effectiveness against IBS-D and the underlying mechanism remain unknown.Experimental Approach: In this study, we established an IBS-D model in rats through chronic restraint stress. We administered the rats with 5, 10, and 20 mg/kg of PA for intestinal and visceral examinations. The longitudinal muscle myenteric plexus (LMMP) neurons were further immunohistochemically stained for quantitative, morphological, and neurotransmitters analyses.Key Results: We found that PA decreased visceral sensitivity, diarrhea symptoms and intestinal transit in the IBS-D rats. Meanwhile, 10 and 20 mg/kg of PA significantly reduced the proportion of excitatory LMMP neurons in the distal colon, decreased the number of acetylcholine (Ach)- and substance P (SP)-positive neurons in the distal colon and restored the levels of Ach and SP in the IBS-D rats.Conclusion and Implications: These findings indicated that PA modulated LMMP excitatory neuron activities, improved intestinal motility and alleviated IBS-induced diarrheal symptoms, suggesting the potential therapeutic efficacy of PA against IBS-D.

With the growing demands of human beings, sanitary landfill, along with the increase in landfill depth and leachate water pressure, has put forward new and higher requirements for the impermeable layer. In particular, it is required to have a certain adsorption capacity of harmful substances from the perspective of environmental protection. Hence, the impermeability of polymer bentonite–sand mixtures (PBTS) at different water pressure and the adsorption properties of polymer bentonite (PBT) on contaminants were investigated through the modification of PBT using betaine compounded with sodium polyacrylate (SPA). It was found that the composite modification of betaine and SPA could reduce the average particle size of PBT dispersed in water (reduced to 106 nm from 201 nm) and enhance the swelling properties. As the content of SPA increased, the hydraulic conductivity of PBTS system decreases and the permeability resistance improves, while the resistance to external water pressure increases. It is proposed a concept of the potential of osmotic pressure in a constrained space to explain the impermeability mechanism of PBTS. The potential of osmotic pressure obtained by linear extrapolation of the trendline of colloidal osmotic pressure versus mass content of PBT could represent the external water pressure that the PBT resist. Additionally, the PBT also has a high adsorption capacity for both organic pollutants and heavy metal ions. The adsorption rate of PBT was up to 99.36% for phenol; up to 99.9% for methylene blue; and 99.89%, 99.9%, and 95.7% for low concentrations of Pb2+, Cd2+, and Hg+, respectively. This work is expected to provide strong technical support for the future development in the field of impermeability and removal of hazardous substances (organic and heavy metals).

Here, we present a high repetition rate, narrow bandwidth, extreme ultraviolet photon source for time- and angle-resolved photoemission spectroscopy. The narrow bandwidth pulses Δ E = 9 , 14 , and 18 meV for photon energies h ν = 10.8 , 18.1 , and 25.3 eV are generated through high harmonic generation using ultra-violet drive pulses with relatively long pulse lengths (461 fs). The high harmonic generation setup employs an annular drive beam in tight focusing geometry at a repetition rate of 250 kHz. Photon energy selection is provided by a series of selectable multilayer bandpass mirrors and thin film filters, thus avoiding any time broadening introduced by single grating monochromators. A two stage optical-parametric amplifier provides

Basket-Sensitive Item Recommendation (BSIR) is a challenging task that aims to recommend an item to add to the current basket given a user’s historical behaviors. The recommended item is supposed to be relevant to the items in current basket. Previous works mainly produce a recommendation based on user’s current basket, ignoring the inherent preference released by user’s long-term behaviors and failing to accurately distinguish the item importance in the basket for detecting user intent. To tackle the above issues, we propose a hybrid model, i.e., Hybrid-Preference Neural Model (HPNM), where a user’s inherent preference is recognized by modeling the historical sequential baskets and the recent preference is identified by focusing on the current basket. In detail, we apply an attention mechanism for distinguishing the importance of items in a basket to generate an accurate basket representation. GRU is utilized for modeling the basket-level sequential information to obtain user’s long-term preference and the representation of the current session is regarded as user’s short-term preference. We evaluate the performance of our proposals against the state-of-the-art baselines in the field of BSIR on two public datasets, i.e., TaFeng and Foursquare. The experimental results show that HPNM can achieve obvious improvements against the baselines in terms of HLU and Recall. In addition, we find HPNM with an attention mechanism can lead to a larger improvement against the baseline for item recommendation in terms of HLU and Recall on testing baskets with relatively fewer items.

Session-based recommendation aims to model a user’s intent and predict an item that the user may interact with in the next step based on an ongoing session. Existing session-based recommender systems mainly aim to model the sequential signals based on Recurrent Neural Network (RNN) structures or the item transition relations between items with Graph Neural Network (GNN) based frameworks to identify a user’s intent for recommendation. However, in real scenarios, there may be strong sequential signals existing in users’ adjacent behaviors or multi-step transition relations among different items. Thus, either RNN- or GNN-based methods can only capture limited information for modeling complex user behavior patterns. RNNs pay attention to the sequential relations among consecutive items, while GNNs focus on structural information, i.e., how to enrich the item embedding with its adjacent items. In this paper, we propose a Collaborative Co-attention Network for Session-based Recommendation (CCN-SR) to incorporate both sequential and structural information, as well as capture the co-relations between them for obtaining an accurate session representation. To be specific, we first model the ongoing session with an RNN structure to capture the sequential information among items. Meanwhile, we also construct a session graph to learn the item representations with a GNN structure. Then, we design a co-attention network upon these two structures to capture the mutual information between them. The designed co-attention network can enrich the representation of each node in the session with both sequential and structural information, and thus generate a more comprehensive representation for each session. Extensive experiments are conducted on two public e-commerce datasets, and the results demonstrate that our proposed model outperforms state-of-the-art baseline model for session based recommendation in terms of both Recall and MRR. We also investigate different combination strategies and the experimental results verify the effectiveness of our proposed co-attention mechanism. Besides, our CCN-SR model achieves better performance than baseline models with different session lengths.

Session-based recommendation (SBRS) aims to make recommendations for users merely based on the ongoing session. Existing GNN-based methods achieve satisfactory performance by exploiting the pair-wise item transition pattern; however, they ignore the temporal evolution of the session graphs over different time-steps. Moreover, the widely applied cross-entropy loss with softmax in SBRS faces the serious overfitting problem. To deal with the above issues, we propose dynamic graph learning for session-based recommendation (DGL-SR). Specifically, we design a dynamic graph neural network (DGNN) to simultaneously take the graph structural information and the temporal dynamics into consideration for learning the dynamic item representations. Moreover, we propose a corrective margin softmax (CMS) to prevent overfitting in the model optimization by correcting the gradient of the negative samples. Comprehensive experiments are conducted on two benchmark datasets, that is, Diginetica and Gowalla, and the experimental results show the superiority of DGL-SR over the state-of-the-art baselines in terms of Recall@20 and MRR@20, especially on hitting the target item in the recommendation list.

BACKGROUND:Oxygen consumption reflects multiple processes in pancreatic islets including mechanisms contributing to insulin secretion, oxidative stress and viability, providing an important readout in studies of islet function, islet viability and drug testing. Due to the scarcity, heterogeneity, and intrinsic kinetic properties of individual islets, it would be of great benefit to detect oxygen consumption by single islets. We present a novel method we have developed to image oxygen in single islets. METHODOLOGY/PRINCIPAL FINDINGS:Using a microfluidics system, individual islets and a fluorescent oxygen-sensitive dye were encased within a thin alginate polymer layer. Insulin secretion by the encapsulated islets was normal. Fluorescent signal from the encased dye, detected using a standard inverted fluorescence microscope and digital camera, was stable and proportional to the amount of oxygen in the media. When integrated into a perifusion system, the sensing system detected changes in response to metabolic substrates, mitochondrial poisons, and induced-oscillations. Glucose responses averaged 30.1±7.1% of the response to a metabolic inhibitor (cyanide), increases were observed in all cases (n = 6), and the system was able to resolve changes in oxygen consumption that had a period greater than 0.5 minutes. The sensing system operated similarly from 2-48 hours following encapsulation, and viability and function of the islets were not significantly affected by the encapsulation process. CONCLUSIONS/SIGNIFICANCE:An oxygen-dependent dye situated around and within a pancreatic islet encapsulated by a thin layer of alginate was sensitive to changes in oxygen consumption, and was not harmful to the function or viability of islets over the course of two days. The microcapsule-based sensing method is particularly suited to assessing the effects of compounds (dose responses and time courses) and chronic changes occurring over the course of days. The approach should be applicable to other cell types and dyes sensitive to other biologically important molecules.

Background and Purpose: Irritable bowel syndrome (IBS) is usually associated with chronic gastrointestinal disorders. Its most common subtype is accompanied with diarrhea (IBS-D). The enteric nervous system (ENS) modulates major gastrointestinal motility and functions whose aberration may induce IBS-D. The enteric neurons are susceptible to long-term neurotransmitter level alterations. The patchouli alcohol (PA), extracted from Pogostemonis Herba, has been reported to regulate neurotransmitter release in the ENS, while its effectiveness against IBS-D and the underlying mechanism remain unknown. Experimental Approach: In this study, we established an IBS-D model in rats through chronic restraint stress. We administered the rats with 5, 10, and 20 mg/kg of PA for intestinal and visceral examinations. The longitudinal muscle myenteric plexus (LMMP) neurons were further immunohistochemically stained for quantitative, morphological, and neurotransmitters analyses. Key Results: We found that PA decreased visceral sensitivity, diarrhea symptoms and intestinal transit in the IBS-D rats. Meanwhile, 10 and 20 mg/kg of PA significantly reduced the proportion of excitatory LMMP neurons in the distal colon, decreased the number of acetylcholine (Ach)- and substance P (SP)-positive neurons in the distal colon and restored the levels of Ach and SP in the IBS-D rats. Conclusion and Implications: These findings indicated that PA modulated LMMP excitatory neuron activities, improved intestinal motility and alleviated IBS-induced diarrheal symptoms, suggesting the potential therapeutic efficacy of PA against IBS-D. [ABSTRACT FROM AUTHOR]

The purpose of this study is to examine the relationship between data analytics techniques and financial statement audits. We analyze how specific data analytics techniques can be applied to financial statement audits and integrate related studies on data analytics in auditing. We provide a comprehensive analysis of how specific data analytics is associated with audit quality and/or auditor’s judgment performance. Research implications and new insights into future research opportunities are accordingly derived. [ABSTRACT FROM AUTHOR]