YTHDF2 favors protumoral macrophage polarization and implies poor survival outcomes in triple negative breast cancer.

Patients with triple-negative breast cancer (TNBC) frequently experience resistance to chemotherapy, leading to recurrence. The approach of optimizing anti-tumoral immunological effect is promising in overcoming such resistance, given the heterogeneity and lack of biomarkers in TNBC. In this study, we focused on YTHDF2, an N6-methyladenosine (m6A) RNA-reader protein, in macrophages, one of the most abundant intra-tumoral immune cells. Using single-cell sequencing and ex vivo experiments, we discovered that YTHDF2 significantly promotes pro-tumoral phenotype polarization of macrophages and is closely associated with down-regulated antigen-presentation signaling to other immune cells in TNBC. The in vitro deprivation of YTHDF2 favors anti-tumoral effect. Expressions of multiple transcription factors, especially SPI1, were consistently observed in YTHDF2-high macrophages, providing potential therapeutic targets for new strategies. In conclusion, YTHDF2 in macrophages appears to promote pro-tumoral effects while suppressing immune activity, indicating the treatment targeting YTHDF2 or its transcription factors could be a promising strategy for chemoresistant TNBC.

Road Network-Guided Fine-Grained Urban Traffic Flow Inference.

Accurate inference of fine-grained traffic flow from coarse-grained one is an emerging yet crucial problem, which can help greatly reduce the number of the required traffic monitoring sensors for cost savings. In this work, we note that traffic flow has a high correlation with road network, which was either completely ignored or simply treated as an external factor in previous works. To facilitate this problem, we propose a novel road-aware traffic flow magnifier (RATFM) that explicitly exploits the prior knowledge of road networks to fully learn the road-aware spatial distribution of fine-grained traffic flow. Specifically, a multidirectional 1-D convolutional layer is first introduced to extract the semantic feature of the road network. Subsequently, we incorporate the road network feature and coarse-grained flow feature to regularize the short-range spatial distribution modeling of road-relative traffic flow. Furthermore, we take the road network feature as a query to capture the long-range spatial distribution of traffic flow with a transformer architecture. Benefiting from the road-aware inference mechanism, our method can generate high-quality fine-grained traffic flow maps. Extensive experiments on three real-world datasets show that the proposed RATFM outperforms state-of-the-art models under various scenarios. Our code and datasets are released at https://github.com/luimoli/RATFM.

Towards Causality-Aware Inferring: A Sequential Discriminative Approach for Medical Diagnosis.

Medical diagnosis assistant (MDA) aims to build an interactive diagnostic agent to sequentially inquire about symptoms for discriminating diseases. However, since the dialogue records for building a patient simulator are collected passively, the collected records might be deteriorated by some task-unrelated biases, such as the preference of the collectors. These biases might hinder the diagnostic agent to capture transportable knowledge from the simulator. This work identifies and resolves two representative non-causal biases, i.e., (i) default-answer bias and (ii) distributional inquiry bias. Specifically, Bias (i) originates from the patient simulator which tries to answer the unrecorded inquiries with some biased default answers. To eliminate this bias and improve upon a well-known causal inference technique, i.e., propensity score matching, we propose a novel propensity latent matching in building a patient simulator to effectively answer unrecorded inquiries; Bias (ii) inherently comes along with the passively collected data that the agent might learn by remembering what to inquire within the training data while not able to generalize to the out-of-distribution cases. To this end, we propose a progressive assurance agent, which includes the dual processes accounting for symptom inquiry and disease diagnosis respectively. The diagnosis process pictures the patient mentally and probabilistically by intervention to eliminate the effect of the inquiry behavior. And the inquiry process is driven by the diagnosis process to inquire about symptoms to enhance the diagnostic confidence which alters as the patient distribution changes. In this cooperative manner, our proposed agent can improve upon the out-of-distribution generalization significantly. Extensive experiments demonstrate that our framework achieves new state-of-the-art performance and possesses the advantage of transportability.

Assessment of Visit-to-Visit Blood Pressure Variability in Adults With Optimal Blood Pressure: A New Player in the Evaluation of Residual Cardiovascular Risk?

Background There is a paucity of evidence regarding the association between visit-to-visit blood pressure variability and residual cardiovascular risk. We aimed to provide relevant evidence by determining whether high systolic blood pressure (SBP) variability in the optimal SBP levels still influences the risk of cardiovascular disease. Methods and Results We studied 7065 participants (aged 59.3±5.6 years; 44.3% men; and 82.9% White) in the ARIC (Atherosclerosis Risk in Communities) study with optimal SBP levels from visit 1 to visit 3. Visit-to-visit SBP variability was measured by variability independent of the mean in the primary analysis. The primary outcome was the major adverse cardiovascular event (MACE), defined as the first occurrence of all-cause mortality, coronary heart disease, stroke, and heart failure. During a median follow-up of 19.6 years, 2691 participants developed MACEs. After multivariable adjustment, the MACE risk was higher by 21% in participants with the highest SBP variability (variability independent of the mean quartile 4) compared with the lowest SBP variability participants (variability independent of the mean quartile 1) (hazard ratio, 1.21; 95% CI, 1.09-1.35). The restricted cubic spline showed that the hazard ratio for MACE was relatively linear, with a higher variability independent of the mean being associated with higher risk. These association were also found in the stratified analyses of participants with or without hypertension. Conclusions In adults with optimal SBP levels, higher visit-to-visit SBP variability was significantly associated with a higher risk of MACE regardless of whether they had hypertension. Therefore, it may be necessary to further focus on the visit-to-visit SBP variability even at the guideline-recommended optimal blood pressure levels.

Deductive Reinforcement Learning for Visual Autonomous Urban Driving Navigation.

Existing deep reinforcement learning (RL) are devoted to research applications on video games, e.g., The Open Racing Car Simulator (TORCS) and Atari games. However, it remains under-explored for vision-based autonomous urban driving navigation (VB-AUDN). VB-AUDN requires a sophisticated agent working safely in structured, changing, and unpredictable environments; otherwise, inappropriate operations may lead to irreversible or catastrophic damages. In this work, we propose a deductive RL (DeRL) to address this challenge. A deduction reasoner (DR) is introduced to endow the agent with ability to foresee the future and to promote policy learning. Specifically, DR first predicts future transitions through a parameterized environment model. Then, DR conducts self-assessment at the predicted trajectory to perceive the consequences of current policy resulting in a more reliable decision-making process. Additionally, a semantic encoder module (SEM) is designed to extract compact driving representation from the raw images, which is robust to the changes of the environment. Extensive experimental results demonstrate that DeRL outperforms the state-of-the-art model-free RL approaches on the public CAR Learning to Act (CARLA) benchmark and presents a superior performance on success rate and driving safety for goal-directed navigation.

Discontinuous polyostotic fibrous dysplasia with multiple systemic disorders and unique genetic mutations: A case report.

BACKGROUND: Polyostotic fibrous dysplasia (PFD) is an uncommon developmental bone disease in which normal bone and marrow are replaced by pseudotumoral tissue. The etiology of PFD is unclear, but it is generally thought to be caused by sporadic, post-zygotic mutations in the GNAS gene. Herein, we report the case of a young female with bone pain and lesions consistent with PFD, unique physical findings, and gene mutations. CASE SUMMARY: A 27-year-old female presented with unbearable bone pain in her left foot for 4 years. Multiple bone lesions were detected by radiographic examinations, and a diagnosis of PFD was made after a biopsy of her left calcaneus with symptoms including pre-axial polydactyly on her left hand and severe ophthalmological problems such as high myopia, vitreous opacity, and choroidal atrophy. Her serum cortisol level was high, consistent with Cushing syndrome. Due to consanguineous marriage of her grandparents, boosted whole exome screening was performed to identify gene mutations. The results revealed mutations in HSPG2 and RIMS1, which may be contributing factors to her unique findings. CONCLUSION: The unique findings in this patient with PFD may be related to mutations in the HSPG2 and RIMS1 genes.

Correlation between microbial communities and key flavors during post-fermentation of Pixian broad bean paste.

Pixian broad bean paste (PBP) is a traditional Chinese condiment, famous for its distinctive flavor. Microbial communities play a vital role in producing the unique flavor of PBP, and a significant accumulation of these volatile flavors occurs during the post-fermentation stage of its production. However, little is known about the relationship between the microbes and flavor compounds in PBP. In this study, high-throughput sequencing (HTS) analysis revealed that Leuconostoc (8.30%), Lactobacillus (7.05%), Weissella (5.80%) and Staphylococcus (4.03%) were the dominant bacterial genera, while the most prevalent yeast genera were Zygosaccharomyces (41.45%) and Pichia (5.83%). Gradual accumulations of free amino acids (glutamic acid and asparagine), organic acids (malic acid and tartaric acid), and unique volatiles (aldehydes, phenols and pyrazines) were evident throughout the post-fermentation process. Analysis of the Pearson's correlation coefficients between 66 key microbes and the key flavors was investigated. Nine core microbes were identified based on the linear discriminant analysis (LDA) scores ≥ 4 (or an average abundance >0.1%) and a high correlation with at least two flavor categories (P < 0.05, |ρ| > 0.8), namely Kosakonia, Kazachstania, Debaryomyces, Lactobacillus, Myroides, Stenotrophomonas, Ochrobactrum, Wohlfahrtiimonas, and Lactococcus genera. These results provide a clearer insight into microbial succession during PBP post-fermentation, thereby contributing to further quality improvement of PBP.

Intra-Articular Steroid Injection for Patients with Hip Osteoarthritis: A Systematic Review and Meta-Analysis.

PURPOSE: The aim of this current review was to confirm the efficacy of intra-articular steroid therapy (IAST) for patients with hip osteoarthritis (OA) and discuss the duration and influential factors of IAST. METHODS: Online databases (Medline, EMBASE, and Web of Science) were searched from inception to May 2019. Both randomized controlled trials (RCTs) and noncontrolled trials assessing the efficacy of hip IAST on pain were included. Common demographics data were extracted using a standardized form. Quality was assessed on the basis of Oxford Centre for Evidence-Based Medicine 2011 Levels of Evidence. RESULTS: 12 trials met the inclusion criteria. According to data from individual trials, IAST had significant efficacy on hip OA in both immediate and delay pain reduction, which persisted up to 12 weeks after IAST. The influences of the baseline severity of hip OA or synovitis and injection dose or volume on the clinical outcome of IAST were still controversial. The IAST appeared to be well tolerant by most of the participants. CONCLUSION: IAST was proved to be an efficacious therapy in both immediate and delay pain reduction for hip OA patients within 12 weeks. The longer follow-up data of efficacy and safety and potentially influential factors are still unclear and needed further confirmation.

Food-Grade Nanoemulsions: Preparation, Stability and Application in Encapsulation of Bioactive Compounds.

Nanoemulsions have attracted significant attention in food fields and can increase the functionality of the bioactive compounds contained within them. In this paper, the preparation methods, including low-energy and high-energy methods, were first reviewed. Second, the physical and chemical destabilization mechanisms of nanoemulsions, such as gravitational separation (creaming or sedimentation), flocculation, coalescence, Ostwald ripening, lipid oxidation and so on, were reviewed. Then, the impact of different stabilizers, including emulsifiers, weighting agents, texture modifiers (thickening agents and gelling agents), ripening inhibitors, antioxidants and chelating agents, on the physicochemical stability of nanoemulsions were discussed. Finally, the applications of nanoemulsions for the delivery of functional ingredients, including bioactive lipids, essential oil, flavor compounds, vitamins, phenolic compounds and carotenoids, were summarized. This review can provide some reference for the selection of preparation methods and stabilizers that will improve performance in nanoemulsion-based products and expand their usage.

Effects of Different Fermentation Strains on the Flavor Characteristics of Fermented Soybean Curd.

The effects of different fermentation strains on the flavor characteristics of fermented soybean curd (FSC) were investigated in this study. Fresh tofu was fermented by Actinomucor elegans, Rhizopus arrhizus, Mucor racemosus, and Rhizopus chinensis, either alone or in various combinations. The FSC manufacturing process included prefermentation by different strains at 28 °C for 60 hr, followed by salting at 16 °C for 7 days and finally proceeding postfermentation at 25 °C for 35 days. Subsequently, five tested samples were obtained, namely, sample A (fermented by A. elegans alone), R (fermented by R. arrhizus alone), AR (fermented by A. elegans and R. arrhizus at 5:1), AM (fermented by A. elegans and M. racemosus at 1:1), and RR (fermented by R. arrhizus and R. chinensis at 7:3). The flavors of the five samples were determined by E-nose, sensory evaluation, and GC-MS. E-nose system observed significant discriminations by principal component analysis and linear discriminant analysis analysis. Sensory evaluation ranked the overall sensory scores: AR>AM>A>RR>R. As shown in GC-MS results, sample AR also had, on average, the highest level of many volatiles. Out of 10 critical volatiles, the detected frequency of samples AR, AM, RR, A, and R was 10, 9, 9, 8, and 7, respectively. PLS2 regression model was used to explore the influence on flavor quality of different strains. All three analytic methods revealed similar results, with sample AR providing the best flavor quality, while the opposite was the case with sample R. Therefore, it could be concluded that A. elegans and R. arrhizus at 5:1 (v/v) was the optimal combination, and may likely promote the production of critical volatile compounds. PRACTICAL APPLICATION: The flavors of fermented soybean curds are influenced by various factors such as physicochemical and microorganism during the fermentation surroundings. The results of this work not only provide valuable information for FSC flavor studies, but can also guide the FSC industry to improve flavor quality by applying the most appropriate production strains.