Early life growth patterns persist for 12 years and impact pulmonary outcomes in cystic fibrosis

BACKGROUND: In children with cystic fibrosis (CF), recovery from growth faltering within 2 years of diagnosis (Responders) is associated with better growth and less lung disease at age 6 years. This study examined whether these benefits are sustained through 12 years of age. METHODS: Longitudinal growth from 76 children with CF enrolled in the Wisconsin CF Neonatal Screening Project was examined and categorized into 5 groups: R12, R6, and R2, representing Responders who maintained growth improvement to age 12, 6, and 2 years, respectively, and I6 and N6, representing Non-responders whose growth did and did not improve during ages 2-6 years, respectively. Lung disease was evaluated by % predicted forced expiratory volume in one second (FEV1) and chest radiograph (CXR) scores. RESULTS: Sixty-two percent were Responders. Within this group, 47% were R12, 28% were R6, and 25% were R2. Among Non-responders, 76% were N6. CF children with meconium ileus (MI) had worse lung function and CXR scores compared to other CF children. Among 53 children with pancreatic insufficiency without MI, R12 had significantly better FEV1 (97-99% predicted) and CXR scores during ages 6-12 years than N6 (89-93% predicted). Both R6 and R2 experienced a decline in FEV1 by ages 10-12 years. CONCLUSIONS: Early growth recovery in CF is critical, as malnutrition during infancy tends to persist and catch-up growth after age 2 years is difficult. The longer adequate growth was maintained after early growth recovery, the better the pulmonary outcomes at age 12 years

Towards Explainable Conversational Recommender Systems

Explanations in conventional recommender systems have demonstrated benefits in helping the user understand the rationality of the recommendations and improving the system's efficiency, transparency, and trustworthiness. In the conversational environment, multiple contextualized explanations need to be generated, which poses further challenges for explanations. To better measure explainability in conversational recommender systems (CRS), we propose ten evaluation perspectives based on concepts from conventional recommender systems together with the characteristics of CRS. We assess five existing CRS benchmark datasets using these metrics and observe the necessity of improving the explanation quality of CRS. To achieve this, we conduct manual and automatic approaches to extend these dialogues and construct a new CRS dataset, namely Explainable Recommendation Dialogues (E-ReDial). It includes 756 dialogues with over 2,000 high-quality rewritten explanations. We compare two baseline approaches to perform explanation generation based on E-ReDial. Experimental results suggest that models trained on E-ReDial can significantly improve explainability while introducing knowledge into the models can further improve the performance. GPT-3 in the in-context learning setting can generate more realistic and diverse movie descriptions. In contrast, T5 training on E-ReDial can better generate clear reasons for recommendations based on user preferences. E-ReDial is available at https://github.com/Superbooming/E-ReDial

Effect of smoking status on total energy expenditure

Individuals who smoke generally have a lower body mass index (BMI) than nonsmokers. The relative roles of energy expenditure and energy intake in maintaining the lower BMI, however, remain controversial. We tested the hypothesis that current smokers have higher total energy expenditure than never smokers in 308 adults aged 40-69 years old of which 47 were current smokers. Energy expenditure was measured by doubly labeled water during a two week period in which the subjects lived at home and performed their normal activities. Smoking status was determined by questionnaire. There were no significant differences in mean BMI (mean ± SD) between smokers and never smokers for either males (27.8+5.1 kg/m2 vs. 27.5+4.0 kg/m2) or females (26.5+5.3 kg/m2 vs. 28.1+6.6 kg/m2), although the difference in females was of similar magnitude to previous reports. Similarly, total energy expenditure of male smokers (3069+764 kcal/d) was not significantly different from that of never smokers (2854+468 kcal/d), and that of female smokers (2266+387 kcal/d) was not different from that of never smokers (2330+415 kcal/d). These findings did not change after adjustment for age, fat-free mass and self-reported physical activity. Using doubly labeled water, we found no evidence of increased energy expenditure among smokers, however, it should be noted that BMI differences in this cohort also did not differ by smoking status

Debiasing Sequential Recommenders through Distributionally Robust Optimization over System Exposure

Sequential recommendation (SR) models are typically trained on user-item interactions which are affected by the system exposure bias, leading to the user preference learned from the biased SR model not being fully consistent with the true user preference. Exposure bias refers to the fact that user interactions are dependent upon the partial items exposed to the user. Existing debiasing methods do not make full use of the system exposure data and suffer from sub-optimal recommendation performance and high variance. In this paper, we propose to debias sequential recommenders through Distributionally Robust Optimization (DRO) over system exposure data. The key idea is to utilize DRO to optimize the worst-case error over an uncertainty set to safeguard the model against distributional discrepancy caused by the exposure bias. The main challenge to apply DRO for exposure debiasing in SR lies in how to construct the uncertainty set and avoid the overestimation of user preference on biased samples. Moreover, how to evaluate the debiasing effect on biased test set is also an open question. To this end, we first introduce an exposure simulator trained upon the system exposure data to calculate the exposure distribution, which is then regarded as the nominal distribution to construct the uncertainty set of DRO. Then, we introduce a penalty to items with high exposure probability to avoid the overestimation of user preference for biased samples. Finally, we design a debiased self-normalized inverse propensity score (SNIPS) evaluator for evaluating the debiasing effect on the biased offline test set. We conduct extensive experiments on two real-world datasets to verify the effectiveness of the proposed methods. Experimental results demonstrate the superior exposure debiasing performance of proposed methods. Codes and data are available at \url{https://github.com/nancheng58/DebiasedSR_DRO}.Comment: Accept by WSDM 202

Enhanced Generative Recommendation via Content and Collaboration Integration

Generative recommendation has emerged as a promising paradigm aimed at augmenting recommender systems with recent advancements in generative artificial intelligence. This task has been formulated as a sequence-to-sequence generation process, wherein the input sequence encompasses data pertaining to the user's previously interacted items, and the output sequence denotes the generative identifier for the suggested item. However, existing generative recommendation approaches still encounter challenges in (i) effectively integrating user-item collaborative signals and item content information within a unified generative framework, and (ii) executing an efficient alignment between content information and collaborative signals. In this paper, we introduce content-based collaborative generation for recommender systems, denoted as ColaRec. To capture collaborative signals, the generative item identifiers are derived from a pretrained collaborative filtering model, while the user is represented through the aggregation of interacted items' content. Subsequently, the aggregated textual description of items is fed into a language model to encapsulate content information. This integration enables ColaRec to amalgamate collaborative signals and content information within an end-to-end framework. Regarding the alignment, we propose an item indexing task to facilitate the mapping between the content-based semantic space and the interaction-based collaborative space. Additionally, a contrastive loss is introduced to ensure that items with similar collaborative GIDs possess comparable content representations, thereby enhancing alignment. To validate the efficacy of ColaRec, we conduct experiments on three benchmark datasets. Empirical results substantiate the superior performance of ColaRec

Rhinovirus increases Moraxella catarrhalis adhesion to the respiratory epithelium

Rhinovirus causes many types of respiratory illnesses, ranging from minor colds to exacerbations of asthma. Moraxella catarrhalis is an opportunistic pathogen that is increased in abundance during rhinovirus illnesses and asthma exacerbations and is associated with increased severity of illness through mechanisms that are ill-defined. We used a co-infection model of human airway epithelium differentiated at the air-liquid interface to test the hypothesis that rhinovirus infection promotes M. catarrhalis adhesion and survival on the respiratory epithelium. Initial experiments showed that infection with M. catarrhalis alone did not damage the epithelium or induce cytokine production, but increased trans-epithelial electrical resistance, indicative of increased barrier function. In a co-infection model, infection with the more virulent rhinovirus-A and rhinovirus-C, but not the less virulent rhinovirus-B types, increased cell-associated M. catarrhalis. Immunofluorescent staining demonstrated that M. catarrhalis adhered to rhinovirus-infected ciliated epithelial cells and infected cells being extruded from the epithelium. Rhinovirus induced pronounced changes in gene expression and secretion of inflammatory cytokines. In contrast, M. catarrhalis caused minimal effects and did not enhance RV-induced responses. Our results indicate that rhinovirus-A or C infection increases M. catarrhalis survival and cell association while M. catarrhalis infection alone does not cause cytopathology or epithelial inflammation. Our findings suggest that rhinovirus and M. catarrhalis co-infection could promote epithelial damage and more severe illness by amplifying leukocyte inflammatory responses at the epithelial surface

Smithian platform-bearing gondolellid conodonts from Yiwagou Section, northwestern China and implications for their geographic distribution in the Early Triassic

Abundant platform-bearing gondolellid conodonts, including Scythogondolella mosheri (Kozur and Mostler), Sc. phryna Orchard and Zonneveld, and Sc. cf. milleri (Müller), have been discovered from the Yiwagou Section of Tewo, together with Novispathodus waageni waageni (Sweet) and Nv. w. eowaageni Zhao and Orchard. This is the first report of Smithian platform-bearing gondolellids from the Paleo-Tethys region. In addition, Eurygnathodus costatus Staesche, E. hamadai(Koike), Parafurnishius xuanhanensis Yang et al., and the genera Pachycladina Staesche, Parachirognathus Clark, and Hadrodontina Staesche have also been recovered from Dienerian to Smithian strata at Yiwagou Section. Three conodont zones are established, in ascending order: Eurygnathodus costatus-E. hamadai Assemblage Zone, Novispathodus waageni-Scythogondolella mosheri Assemblage Zone, and the Pachycladina-Parachirognathus Assemblage Zone. The platform-bearing gondolellids were globally distributed just after the end-Permian mass extinction, but the formerly abundant Clarkina Kozur disappeared in the late Griesbachian. Platform-bearing gondolellids dramatically decreased to a minimum of diversity and extent in the Dienerian before recovering in the Smithian. Scythogondolella Kozur, probably a thermophilic and eurythermic genus, lived in all latitudes at this time whereas other genera did not cope with Smithian high temperatures and so became restricted to the high-latitude regions. However, the maximum temperature in the late Smithian likely caused the extinction of almost all platform-bearing gondolellids. Finally, the group returned to equatorial regions and achieved global distribution again in the cooler conditions of the late Spathian. We conclude that temperature (and to a lesser extent oxygen levels) exerted a strong control on the geographical distribution and evolution of platform-bearing gondolellids in the Early Triassic

Membership Inference Attacks Against Recommender Systems

Recently, recommender systems have achieved promising performances and become one of the most widely used web applications. However, recommender systems are often trained on highly sensitive user data, thus potential data leakage from recommender systems may lead to severe privacy problems. In this paper, we make the first attempt on quantifying the privacy leakage of recommender systems through the lens of membership inference. In contrast with traditional membership inference against machine learning classifiers, our attack faces two main differences. First, our attack is on the user-level but not on the data sample-level. Second, the adversary can only observe the ordered recommended items from a recommender system instead of prediction results in the form of posterior probabilities. To address the above challenges, we propose a novel method by representing users from relevant items. Moreover, a shadow recommender is established to derive the labeled training data for training the attack model. Extensive experimental results show that our attack framework achieves a strong performance. In addition, we design a defense mechanism to effectively mitigate the membership inference threat of recommender systems

Metaphorical User Simulators for Evaluating Task-oriented Dialogue Systems

Task-oriented dialogue systems (TDSs) are assessed mainly in an offline setting or through human evaluation. The evaluation is often limited to single-turn or very time-intensive. As an alternative, user simulators that mimic user behavior allow us to consider a broad set of user goals to generate human-like conversations for simulated evaluation. Employing existing user simulators to evaluate TDSs is challenging as user simulators are primarily designed to optimize dialogue policies for TDSs and have limited evaluation capability. Moreover, the evaluation of user simulators is an open challenge. In this work, we proposes a metaphorical user simulator for endto-end TDS evaluation. We also propose a tester-based evaluation framework to generate variants, i.e., dialogue systems with different capabilities. Our user simulator constructs a metaphorical user model that assists the simulator in reasoning by referring to prior knowledge when encountering new items. We estimate the quality of simulators by checking the simulated interactions between simulators and variants. Our experiments are conducted using three TDS datasets. The metaphorical user simulator demonstrates better consistency with manual evaluation than Agenda-based simulator and Seq2seq model on three datasets; our tester framework demonstrates efficiency, and our approach demonstrates better generalization and scalability.Comment: There are important errors in the article. We are very sorry, please withdraw it as soon as possibl