Diffusion Recommender Model

Generative models such as Generative Adversarial Networks (GANs) and Variational Auto-Encoders (VAEs) are widely utilized to model the generative process of user interactions. However, these generative models suffer from intrinsic limitations such as the instability of GANs and the restricted representation ability of VAEs. Such limitations hinder the accurate modeling of the complex user interaction generation procedure, such as noisy interactions caused by various interference factors. In light of the impressive advantages of Diffusion Models (DMs) over traditional generative models in image synthesis, we propose a novel Diffusion Recommender Model (named DiffRec) to learn the generative process in a denoising manner. To retain personalized information in user interactions, DiffRec reduces the added noises and avoids corrupting users' interactions into pure noises like in image synthesis. In addition, we extend traditional DMs to tackle the unique challenges in practical recommender systems: high resource costs for large-scale item prediction and temporal shifts of user preference. To this end, we propose two extensions of DiffRec: L-DiffRec clusters items for dimension compression and conducts the diffusion processes in the latent space; and T-DiffRec reweights user interactions based on the interaction timestamps to encode temporal information. We conduct extensive experiments on three datasets under multiple settings (e.g. clean training, noisy training, and temporal training). The empirical results and in-depth analysis validate the superiority of DiffRec with two extensions over competitive baselines.Comment: 11 pages, 7 figures, accepted for publication in SIGIR'2

Tailoring the supramolecular structure of amphiphilic glycopolypeptide analogue toward liver targeted drug delivery systems

Amphiphilic glycopolypeptide analogues have harboured great importance in the development of targeted drug delivery systems. In this study, lactosylated pullulan-graft-arginine dendrons (LP-g-G3P) was synthesized using Huisgen azide-alkyne 1,3-dipolar cycloaddition between lactosylated pullulan and generation 3 arginine dendrons bearing Pbf and Boc groups on the periphery. Hydrophilic lactosylated pullulan was selected for amphiphilic modification, aiming at specific lectin recognition. Macromolecular structure of LP-g-G3P combined alkyl, aromatic, and peptide dendritic hydrophobic moieties and was able to self-assemble spontaneously into core-shell nanoarchitectures with small particle sizes and low polydispersity in the aqueous media, which was confirmed by CAC, DLS and TEM. Furthermore, the polyaromatic anticancer drug (doxorubicin, DOX) was selectively encapsulated in the hydrophobic core through multiple interactions with the dendrons, including π-π interactions, hydrogen bonding and hydrophobic interactions. Such multiple interactions had the merits of enhanced drug loading capacity (16.89 ± 2.41%), good stability against dilution, and excellent sustained release property. The cell viability assay presented that LP-g-G3P nanoparticles had an excellent biocompatibility both in the normal and tumor cells. Moreover, LP-g-G3P/DOX nanoparticles could be effectively internalized into the hepatoma carcinoma cells and dramatically inhibited cell proliferation. Thus, this approach paves the way to develop amphiphilic and biofunctional glycopolypeptide-based drug delivery systems.the European Commission Research and Innovation (PIRSES-GA-2011-295218

Effects of a Cardiotonic Medicine Danshen Pills, on Cognitive Ability and Expression of PSD-95 in a Vascular Dementia Rat Model

A widely used Chinese cardiotonic proprietary medicine, compound Danshen dripping pills (CDDP, Fufang Danshen Diwan) has also begun to be used for treatment of vascular dementia (VaD). We tried to explore the mechanism of CDDP action in this case. A VaD experimental model was built in rats by bilateral ligation of the common carotid arteries. The cognitive ability of experimental animals was evaluated in the Morris water maze test. Synaptic ultrastructural changes in the hippocampus were detected by transmission electron microscopy; expression of PSD-95 mRNA in the hippocampus was examined using hybridization in situ. The latter index (mRNA expression) in the VaD group was significantly lower than those in the CDDP and shamoperated groups (P < 0.05). CDDP treatment considerably improved disturbed ultrastructural synaptic characteristics in the hippocampus of VaD rats. The mean escape latency in the Morris water maze test was significantly shorter in CDDP-treated VaD rats, compared with that those of the VD group (P < 0.05). In the CDDP group compared to the VaD one, escape strategies improved from edge and random searches to more linear swim pathway (P < 0.05). Thus, decreasing expression of PSD-95 plays an important role in the pathogenesis of VaD. CDDP treatment improves the learning and memory ability of VaD rats by improving neural synaptic ultrastructural characteristics and increasing expression of PSD-95 mRNA in the hippocampus.Широко вживаний у Китаї патентований кардіотонічний засіб «складні пілюлі Даншен» (CDDP) почав також використовуватися для лікування васкулярної деменції (ВД). Ми досліджували можливі механізми дії цього засобу в даному аспекті. ВД моделювали у щурів, застосовуючи білатеральну перев’язку загальних сонних артерій. Когнітивні здатності експериментальних тварин оцінювали в тесті водного лабіринту Морріса. Ультраструктурні зміни синаптичних утворень у гіпокампі спостерігали, використовуючи трансмісійну електронну мікроскопію. Експресію мРНК білка PSD-95 у гіпокампі оцінювали із застосуванням методики гібридизації in situ. Останній показник (експресія мРНК) у щурів групи ВД був вірогідно нижчим, ніж у тварин контрольної групи та щурів із ВД, лікованих за допомогою CDDP. Середня затримка реакції уникання у тварин групи ВД істотно перевищувала відповідне значення в групі CDDP (P < 0.05). Стратегії уникання в останній групі були вірогідно кращими, ніж у групі ВД (збільшувалася пропорція лінійних маршрутів порівняно з «крайовими» та випадковими; P < 0.05). Зроблено висновок, що зниження експресії PSD-95 відіграє важливу роль у патогенезі ВД. Лікувальний ефект CDDP забезпечує покращення пам’яті та здатності до навчання у щурів з ВД; цей ефект опосередковується покращенням ультраструктурних показників синаптичних структур та збільшенням експресії мРНК білка PSD-95 у гіпокампі

Development and internal validation of a nine-lncRNA prognostic signature for prediction of overall survival in colorectal cancer patients

Background Colorectal cancer remains a serious public health problem due to the poor prognosis. In the present study, we attempted to develop and validate a prognostic signature to predict the individual mortality risk in colorectal cancer patients. Materials and Methods The original study datasets were downloaded from The Cancer Genome Atlas database. The present study finally included 424 colorectal cancer patients with wholly gene expression information and overall survival information. Results A nine-lncRNA prognostic signature was built through univariate and multivariate Cox proportional regression model. Time-dependent receiver operating characteristic curves in model cohort demonstrated that the Harrell’s concordance indexes of nine-lncRNA prognostic signature were 0.768 (95% CI [0.717–0.819]), 0.778 (95% CI [0.727–0.829]) and 0.870 (95% CI [0.819–0.921]) for 1-year, 3-year and 5-year overall survival respectively. In validation cohort, the Harrell’s concordance indexes of nine-lncRNA prognostic signature were 0.761 (95% CI [0.710–0.812]), 0.801 (95% CI [0.750–0.852]) and 0.883 (95% CI [0.832–0.934]) for 1-year, 3-year and 5-year overall survival respectively. According to the median of nine-lncRNA prognostic signature score in model cohort, 424 CRC patients could be stratified into high risk group (n = 212) and low risk group (n = 212). Kaplan–Meier survival curves showed that the overall survival rate of high risk group was significantly lower than that of low risk group (P < 0.001). Discussion The present study developed and validated a nine-lncRNA prognostic signature for individual mortality risk assessment in colorectal cancer patients. This nine-lncRNA prognostic signature is helpful to evaluate the individual mortality risk and to improve the decision making of individualized treatments in colorectal cancer patients

Association between Vitamin D Supplementation and Cancer Mortality: A Systematic Review and Meta-Analysis

Background: Vitamin D deficiency is related to increased cancer risk and deaths. However, whether vitamin D supplementation reduces cancer mortality remains unclear, and several randomized controlled trials yield inconsistent results. Methods: Medline, Embase, and the Cochrane Central Register of Controlled Trials were searched from their inception until 28 June 2022, for randomized controlled trials investigating vitamin D supplementation. Pooled relative risks (RRs) and their 95% confidence intervals (CIs) were estimated. Trials with vitamin D supplementation combined with calcium supplementation versus placebo alone and recruiting participants with cancer at baseline were excluded in the present study. Results: This study included 12 trials with a total of 72,669 participants. Vitamin D supplementation did not reduce overall cancer mortality (RR 0.96, 95% CI 0.80-1.16). However, vitamin D supplementation was associated with a reduction in lung cancer mortality (RR 0.63, 95% CI 0.45-0.90). Conclusions: Vitamin D supplementation could not reduce cancer mortality in this highly purified meta-analysis. Further RCTs that evaluate the association between vitamin D supplementation and total cancer mortality are still needed

OmniForce: On Human-Centered, Large Model Empowered and Cloud-Edge Collaborative AutoML System

Automated machine learning (AutoML) seeks to build ML models with minimal human effort. While considerable research has been conducted in the area of AutoML in general, aiming to take humans out of the loop when building artificial intelligence (AI) applications, scant literature has focused on how AutoML works well in open-environment scenarios such as the process of training and updating large models, industrial supply chains or the industrial metaverse, where people often face open-loop problems during the search process: they must continuously collect data, update data and models, satisfy the requirements of the development and deployment environment, support massive devices, modify evaluation metrics, etc. Addressing the open-environment issue with pure data-driven approaches requires considerable data, computing resources, and effort from dedicated data engineers, making current AutoML systems and platforms inefficient and computationally intractable. Human-computer interaction is a practical and feasible way to tackle the problem of open-environment AI. In this paper, we introduce OmniForce, a human-centered AutoML (HAML) system that yields both human-assisted ML and ML-assisted human techniques, to put an AutoML system into practice and build adaptive AI in open-environment scenarios. Specifically, we present OmniForce in terms of ML version management; pipeline-driven development and deployment collaborations; a flexible search strategy framework; and widely provisioned and crowdsourced application algorithms, including large models. Furthermore, the (large) models constructed by OmniForce can be automatically turned into remote services in a few minutes; this process is dubbed model as a service (MaaS). Experimental results obtained in multiple search spaces and real-world use cases demonstrate the efficacy and efficiency of OmniForce