Sequential Recommendation with Diffusion Models

Generative models, such as Variational Auto-Encoder (VAE) and Generative Adversarial Network (GAN), have been successfully applied in sequential recommendation. These methods require sampling from probability distributions and adopt auxiliary loss functions to optimize the model, which can capture the uncertainty of user behaviors and alleviate exposure bias. However, existing generative models still suffer from the posterior collapse problem or the model collapse problem, thus limiting their applications in sequential recommendation. To tackle the challenges mentioned above, we leverage a new paradigm of the generative models, i.e., diffusion models, and present sequential recommendation with diffusion models (DiffRec), which can avoid the issues of VAE- and GAN-based models and show better performance. While diffusion models are originally proposed to process continuous image data, we design an additional transition in the forward process together with a transition in the reverse process to enable the processing of the discrete recommendation data. We also design a different noising strategy that only noises the target item instead of the whole sequence, which is more suitable for sequential recommendation. Based on the modified diffusion process, we derive the objective function of our framework using a simplification technique and design a denoise sequential recommender to fulfill the objective function. As the lengthened diffusion steps substantially increase the time complexity, we propose an efficient training strategy and an efficient inference strategy to reduce training and inference cost and improve recommendation diversity. Extensive experiment results on three public benchmark datasets verify the effectiveness of our approach and show that DiffRec outperforms the state-of-the-art sequential recommendation models

Trends in adverse drug reaction-related hospitalisations over 13 years in New South Wales, Australia

Background Adverse drug reactions (ADR) are severe problems in global public health, and result in high mortality and morbidity. Various trends of ADR‐related hospitalisations have been studied in many countries, while estimates of the trends in Australia are limited. Aim To examine trends in ADR‐related hospitalisations in New South Wales (NSW). Methods Data were extracted from the Admitted Patient Data Collection, a census of hospital separations in NSW. We estimated age‐adjusted rates of ADR‐related hospitalisation between 1 July 2001 and 30 June 2014 and rates by patient characteristics, main therapeutic medication groups and clinical condition groups that warranted the hospitalisation. We used the percentage change annualised estimator to evaluate rates over time. Results A total of 315 274 NSW residents admitted for urgent care of ADR was identified. The age‐adjusted rates of ADR‐related hospitalisations nearly doubled and increased by 5.8% (95% CI: 5.0–6.6%) per annum, with an in‐hospital death rate increase of 2.4% (1.6–3.3%). Agranulocytosis (2.7%), nausea and vomiting (2.4%) and heart failure (2.4%) were the most common conditions that led to ADR‐related hospitalisations over 13 years, with acute renal failure (1.4%) recently emerging as the leading adverse condition. Participants aged between 65 and 84 years accounted for nearly half of ADR hospitalisations (45.6%), with age‐adjusted rate increasing from 103.9 in 2001–2002 to 189.0 per 100 000 NSW residents in 2013–2014. Anticoagulants (13.5%) were the most common medications contributing to ADR‐related hospitalisation, followed by opioid analgesics (9.6%). Conclusion ADR‐related hospitalisation remains a population health burden, with significant increase over time. The findings call for continuing efforts to prevent ADR, especially among high‐risk populations, such as older people.This study was supported by the NHMRC CREMA Small Project Grants scheme

Study on impact of gap difference on plasma distribution of direct current vacuum circuit breaker with double-break

During the fault current breaking process of a mechanical direct current vacuum circuit breaker (DC VCB) with double-break (DB), the mechanism’s dispersion can result in a gap difference between the two breaks. A DB DC VCB breaking experiment platform is constructed in order to investigate the impact of gap difference on plasma distribution during the DB DC VCB breaking process. During the experiment, a high-speed camera is used to capture the vacuum arcing process at the two breaks under varying gap difference conditions. Then, the arc feature parameters and their variations during the zero-zone process are extracted using image processing techniques, and the distribution patterns of plasma and arc energy at the two breaks are analyzed and compared. When there is no gap difference between the two breaks of the experimental DB DC VCB, there are no significant differences in arc energy and the sizes of high-, medium-, and low-temperature plasma zones between the two breaks. When there is a gap difference between two breaks, the break with the smaller gap has larger high and medium-temperature plasma zones, more concentrated arc energy, higher particle concentration, lower arc diffusion velocity and arc energy decay velocity, and a greater amount of residual plasma after the extinguishing of the arc. When the gap difference exceeds a certain threshold, energy spots appear on the contact surfaces, and a high concentration corridor of residual particles remains between the contacts after the current crosses zero, forming a breakdown weak point that eventually leads to arc re-ignition (hence interruption failure) under the action of transient recovery voltage

Influence of Controller’s Parameters on Static Bifurcation of Magnetic-Liquid Double Suspension Bearing

Magnetic-Liquid Double Suspension Bearing (MLDSB) is composed of an electromagnetic supporting and a hydrostatic supporting system. Due to greater supporting capacity and static stiffness, it is appropriate for occasions of middle speed, overloading, and frequent starting. Because of the complicated structure of the supporting system, the probability and degree of static bifurcation of MLDSB can be increased by the coupling of hydrostatic force and electromagnetism force, and then the supporting capacity and operation stability are reduced. As the key part of MLDSB, the controller makes an important impact on its supporting capacity, operation stability, and reliability. Firstly, the mathematical model of MLDSB is established in the paper. Secondly, the static bifurcation point of MLDSB is determined, and the influence of parameters of the controller on singular point characteristics is analyzed. Finally, the influence of parameters of the controller on phase trajectories and basin of attraction is analyzed. The result showed that the pitchfork bifurcation will occur as proportional feedback coefficient Kp increases, and the static bifurcation point is Kp = −60.55. When Kp Kp > −60.55, the supporting system has one unstable saddle (0, 0) and two stable non-null focuses or nodes. The shape of the basin of attraction changed greatly as Kp increases from −60.55 to 30, while the outline of the basin of attraction is basically fixed as Kp increases from 30 to 80. Differential feedback coefficient Kd has no effect on the static bifurcation of MLDSB. The rotor phase trajectory obtained from theoretical simulation and experimental tests are basically consistent, and the error is due to the leakage and damping effect of the hydrostatic system within the allowable range of the engineering. The research in the paper can provide theoretical reference for static bifurcation analysis of MLDSB

Influence of Controller’s Parameters on Static Bifurcation of Magnetic-Liquid Double Suspension Bearing

Magnetic-Liquid Double Suspension Bearing (MLDSB) is composed of an electromagnetic supporting and a hydrostatic supporting system. Due to greater supporting capacity and static stiffness, it is appropriate for occasions of middle speed, overloading, and frequent starting. Because of the complicated structure of the supporting system, the probability and degree of static bifurcation of MLDSB can be increased by the coupling of hydrostatic force and electromagnetism force, and then the supporting capacity and operation stability are reduced. As the key part of MLDSB, the controller makes an important impact on its supporting capacity, operation stability, and reliability. Firstly, the mathematical model of MLDSB is established in the paper. Secondly, the static bifurcation point of MLDSB is determined, and the influence of parameters of the controller on singular point characteristics is analyzed. Finally, the influence of parameters of the controller on phase trajectories and basin of attraction is analyzed. The result showed that the pitchfork bifurcation will occur as proportional feedback coefficient Kp increases, and the static bifurcation point is Kp = −60.55. When Kp < −60.55, the supporting system only has one stable node (0, 0). When Kp > −60.55, the supporting system has one unstable saddle (0, 0) and two stable non-null focuses or nodes. The shape of the basin of attraction changed greatly as Kp increases from −60.55 to 30, while the outline of the basin of attraction is basically fixed as Kp increases from 30 to 80. Differential feedback coefficient Kd has no effect on the static bifurcation of MLDSB. The rotor phase trajectory obtained from theoretical simulation and experimental tests are basically consistent, and the error is due to the leakage and damping effect of the hydrostatic system within the allowable range of the engineering. The research in the paper can provide theoretical reference for static bifurcation analysis of MLDSB

Adaptation of soil micro-food web to elemental limitation: evidence from the forest-steppe ecotone

Stoichiometric imbalance between resources and their decomposers can alter the activity and structure of soil microbial communities and play an important role in regulating nutrient and carbon cycling in terrestrial ecosystems. However, whether and how ecological networks of soil micro-food web change to cope with the stoichiometric imbalance has never been assessed so far. In this study, we tested how the soil micro-food web responded to elemental limitations along a forest-steppe ecotone. We measured several adaptation mechanisms including soil microbial stoichiometry, enzyme activities and the composition of soil microbial and nematode communities. The microbial investment in resource acquisition shifted from nutrient-to C-acquiring enzymes with decreasing soil C:N:P ratios along the forest-steppe ecotone. The shifts in element use efficiencies could be a compensatory way besides enzyme allocation, implemented by microbial communities to cope with stoichiometric imbalance in their substrates. The community structure of soil micro-food web also changed with a decrease in saprotrophic fungi and fungivorous nematodes from the steppe plots towards the forest plots. The cooccurrence networks were less complex and stable with decreasing soil C:N:P ratios, suggesting that trophic interactions were less diverse and C-limitation plays an important role in structuring ecological interactions in soil food webs. Our findings provide insights into how stoichiometric constraints may induce shifts in ecological networks and have important implications for integrating shifts in individual physiological metabolism as well as changes in community composition of soil biota and to better understand and predict soil biogeochemical cycling in terrestrial ecosystems

Microfauna community assembly and cascading relationship with microflora in cropland ecosystems along a latitudinal gradient

Cropland ecosystems are environmentally more homogeneous than natural ecosystems due to anthropogenic management. However, it is unclear whether there is similar convergence in soil fauna diversity and community assembly in croplands with different latitudes. Therefore, the study on the latitudinal pattern and community assembly processes of soil microfauna across a transect spanning 3200 km of croplands was conducted. The results indicated that the & beta; diversity of plant parasites, fungivores, and bacterivores showed a decreasing trend and that of omnivores-predators at higher levels followed a unimodal distribution along a latitudinal gradient. The dissimilarity of soil microfauna communities increased with geographical distance, which exhibited a strong distance decay pattern. The & beta; diversity partitioning analysis suggested that species replacement had the greatest influence for all trophic groups, except for omnivores-predators that were driven by a combination of richness differences and species replacement. The stochastic processes shaping the microfauna assembly prevailed over deterministic processes. Ecological drift and homogenizing dispersal of the stochastic processes accounted for 50.4% and 37% of total variations respectively, and heterogeneous selection of the deterministic processes for 10%. Both soil and climatic factors had a direct impact on soil microflora and plant parasites, and indirect impact on bacterivores, fungivores, and omnivores-predators through mediating microflora communities. The partial least squares path modeling and co-occurrence analysis all showed that the soil microfauna assembly was inextricably linked to soil microflora and their network complexity exhibited a significant increasing trend along the latitudinal gradient. Further studies are needed to optimize the molecular database of soil microfauna and construct models for predicting their community distribution patterns

Computational Investigation of Ligand Binding to the Peripheral Site in CYP3A4: Conformational Dynamics and Inhibitor Discovery

Human cytochrome P450 3A4 (CYP3A4) is a major drug-metabolizing enzyme responsible for the metabolism of ∼50% of clinically used drugs and is often involved in drug–drug interactions. It exhibits atypical binding and kinetic behavior toward many ligands. Binding of ligands to CYP3A4 is a complex process. Recent studies from both crystallography and biochemistry suggested the existence of a peripheral ligand-binding site at the enzyme surface. However, the stability of the ligand bound at this peripheral site and the possibility of discovering new CYP3A4 ligands based on this site remain unclear. In this study, we employed a combination of molecular docking, multiparalleled molecular dynamics (MD) simulations, virtual screening, and experimental bioassay to investigate these issues. Our results revealed that the binding mode of progesterone (PGS), a substrate of CYP3A4, in the crystal structure was not stable and underwent a significant conformational change. Through Glide docking and MD refinement, it was found that PGS was able to stably bind at the peripheral site via contacts with Phe215, Phe219, Phe220, and Asp214. On the basis of the refined peripheral site, virtual screening was then performed against the Enamine database. A total of three compounds were finally found to have inhibitory activity against CYP3A4 in both human liver microsome and recombinant human CYP3A4 enzyme assays, one of which showed potent inhibitory activity with IC₅₀ lower than 1 μM and two of which exhibited moderate inhibitory activity with IC₅₀ values lower than 10 μM. The findings not only presented the dynamic behavior of PGS at the peripheral site but also demonstrated the first indication of discovering CYP3A4 inhibitors based on the peripheral site

Sitting-induced hemodynamic changes and association with sitting intolerance in children and adolescents: a cross-sectional study

Abstract Hemodynamic alteration with postural change from supine to sitting has been unclear in the young. In the cross-sectional study, 686 participants (371 boys and 315 girls, aged 6–18 years) were recruited from 4 schools in Kaifeng city, the central area of China. The active sitting test was performed to obtain heart rate (HR) and blood pressure (BP) changes from supine to sitting in children and adolescents. Hemodynamic change-associated sitting intolerance was analyzed. In the study participants, the 95th percentile (P95) values of changes in HR and BP within 3 min from supine to sitting were 25 beats/min and 18/19 mm Hg, respectively. Sixty-six participants had sitting intolerance symptoms. Compared with participants without sitting intolerance symptoms, those with symptoms more frequently had HR increase ≥ P95 or BP increase ≥ P95 within 3 min from supine to sitting (P < 0.001). Risk factors for sitting intolerance were age (odds ratio 1.218, 95% confidence interval 1.072–1.384, P = 0.002) and changes in HR or BP ≥ P95 within 3 min after sitting (odds ratio 2.902, 95% confidence interval 1.572–5.357, P = 0.001). We firstly showed hemodynamic changing profiles from supine to sitting and their association with sitting intolerance in children and adolescents. Sitting tachycardia is likely suggested with a change in HR ≥ 25 beats/min and sitting hypertension with a change in BP ≥ 20/20 mm Hg when changing from supine to sitting within 3 min. The age and changes in HR or BP were independent risk factors for sitting intolerance

In Silico Prediction of Chemicals Binding to Aromatase with Machine Learning Methods

Environmental chemicals may affect endocrine systems through multiple mechanisms, one of which is via effects on aromatase (also known as CYP19A1), an enzyme critical for maintaining the normal balance of estrogens and androgens in the body. Therefore, rapid and efficient identification of aromatase-related endocrine disrupting chemicals (EDCs) is important for toxicology and environment risk assessment. In this study, on the basis of the Tox21 10K compound library, in silico classification models for predicting aromatase binders/nonbinders were constructed by machine learning methods. To improve the prediction ability of the models, a combined classifier (CC) strategy that combines different independent machine learning methods was adopted. Performances of the models were measured by test and external validation sets containing 1336 and 216 chemicals, respectively. The best model was obtained with the MACCS (Molecular Access System) fingerprint and CC method, which exhibited an accuracy of 0.84 for the test set and 0.91 for the external validation set. Additionally, several representative substructures for characterizing aromatase binders, such as ketone, lactone, and nitrogen-containing derivatives, were identified using information gain and substructure frequency analysis. Our study provided a systematic assessment of chemicals binding to aromatase. The built models can be helpful to rapidly identify potential EDCs targeting aromatase