Generalized Value Iteration Networks: Life Beyond Lattices

In this paper, we introduce a generalized value iteration network (GVIN), which is an end-to-end neural network planning module. GVIN emulates the value iteration algorithm by using a novel graph convolution operator, which enables GVIN to learn and plan on irregular spatial graphs. We propose three novel differentiable kernels as graph convolution operators and show that the embedding based kernel achieves the best performance. We further propose episodic Q-learning, an improvement upon traditional n-step Q-learning that stabilizes training for networks that contain a planning module. Lastly, we evaluate GVIN on planning problems in 2D mazes, irregular graphs, and real-world street networks, showing that GVIN generalizes well for both arbitrary graphs and unseen graphs of larger scale and outperforms a naive generalization of VIN (discretizing a spatial graph into a 2D image).Comment: 14 pages, conferenc

Improving Non-autoregressive Machine Translation with Error Exposure and Consistency Regularization

Being one of the IR-NAT (Iterative-refinemennt-based NAT) frameworks, the Conditional Masked Language Model (CMLM) adopts the mask-predict paradigm to re-predict the masked low-confidence tokens. However, CMLM suffers from the data distribution discrepancy between training and inference, where the observed tokens are generated differently in the two cases. In this paper, we address this problem with the training approaches of error exposure and consistency regularization (EECR). We construct the mixed sequences based on model prediction during training, and propose to optimize over the masked tokens under imperfect observation conditions. We also design a consistency learning method to constrain the data distribution for the masked tokens under different observing situations to narrow down the gap between training and inference. The experiments on five translation benchmarks obtains an average improvement of 0.68 and 0.40 BLEU scores compared to the base models, respectively, and our CMLMC-EECR achieves the best performance with a comparable translation quality with the Transformer. The experiments results demonstrate the effectiveness of our method

Increased IL-10 mRNA expression in tumor-associated macrophage correlated with late stage of lung cancer

<p>Abstract</p> <p>Background</p> <p>Monocyte recruited into the tumor and maturation to tumor-associated macrophage (TAM). <it>Interleukin-10(IL-10) </it>is a potent immunosuppressive cytokine, which can be secreted from both primary tumor and stromal cells. However, there are controversies regarding its role in the progression of cancer. So it is important to isolate TAM from tumor cells to study the role of <it>IL-10 </it>in the progress of cancer. The aim of our study was to determine whether <it>IL-10 </it>expressed by TAM correlated with clinicopathological factors in NSCLC.</p> <p>Methods</p> <p>TAM in NSCLC was isolated by short-term culture in serum free medium with the modification to literature reports. The mRNA expression levels of <it>IL-10</it>, <it>cathepsin B</it>, <it>cathepsin S</it>, which were closely related with TAM according to the literatures, were evaluated by Quantitative real-time RT-PCR in 63 NSCLC. The relationships between their expression levels and clinicopathological features were investigated.</p> <p>Results</p> <p>We successfully achieved up to 95% purity of TAM, derived from 63 primary lung cancer tissues. TAM expressed high levels of <it>IL-10</it>, <it>cathepsin B </it>in NSCLC. High levels of <it>IL-10 </it>in TAM significantly correlated with stage, tumor size, lymph node metastasis, lymphovascular invasion or histologic poor differentiation.</p> <p>Conclusions</p> <p>Our results revealed that TAM with high levels of <it>IL-10 </it>expression may play an important role in the progression of non-small cell lung cancer. The data also suggested that TAMs may involve in tumor immunosuppression through overexpressed <it>IL-10</it>. Additionally, the phenotype of isolated TAM can be potentially used to predict clinicopathological features as well.</p

Paddy-upland rotation improves soil quality by reshaping soil nematode and microbial communities

Paddy–upland rotation systems are widely adopted to mitigate soil degradation in rice-based agroecosystems; however, their impacts on soil biota remain insufficiently understood. This study investigated the impacts of paddy continuous cropping (PA), upland continuous cropping (UP), and rice–loofah paddy–upland rotation (RO) on soil nematodes and microbial communities in southeastern China. Soil samples were collected prior to harvest at the end of the rice season and were analyzed for physicochemical properties, nematode communities via morphological identification, and microbial communities through high-throughput sequencing. The results showed that the RO system significantly increased soil pH, total phosphorus, available potassium, and available phosphorus, while reducing the abundance of the plant-parasitic nematode Hirschmanniella compared to the PA system. The total nematode abundance was highest in the UP system, where bacterivores predominated; the RO system was characterized by a higher proportion of algivores associated with flooded conditions, whereas the PA system was dominated by herbivores. The RO and PA system also improved nematode food web stability under flooded conditions, as indicated by higher maturity and structure indices relative to the UP system. Although microbial diversity did not differ significantly between systems, the community composition and predicted functional groups varied considerably. The relative abundance of Gemmatimonadota was significantly reduced in the PA system, while the abundance of Nitrospirota, Myxococcota, and Entorrhizomycota increased. Functional prediction revealed system-specific enrichment of bacterial metabolic groups associated with nitrogen cycling, carbon turnover, and redox-sensitive energy metabolism. Integration of soil physicochemical and biological indicators into a Soil Quality Index (SQI) ranked RO highest, underscoring its capacity to enhance soil ecological function and sustainability in rice-based systems

Modulation of the Proteostasis Network Promotes Tumor Resistance to Oncogenic KRAS Inhibitors

Despite substantial advances in targeting mutant KRAS, tumor resistance to KRAS inhibitors (KRASi) remains a major barrier to progress. Here, we report proteostasis reprogramming as a key convergence point of multiple KRASi-resistance mechanisms. Inactivation of oncogenic KRAS down-regulated both the heat shock response and the inositol-requiring enzyme 1α (IRE1α) branch of the unfolded protein response, causing severe proteostasis disturbances. However, IRE1α was selectively reactivated in an ER stress-independent manner in acquired KRASi-resistant tumors, restoring proteostasis. Oncogenic KRAS promoted IRE1α protein stability through extracellular signal-regulated kinase (ERK)-dependent phosphorylation of IRE1α, leading to IRE1α disassociation from 3-hydroxy-3-methylglutaryl reductase degradation (HRD1) E3-ligase. In KRASi-resistant tumors, both reactivated ERK and hyperactivated AKT restored IRE1α phosphorylation and stability. Suppression of IRE1α overcame resistance to KRASi. This study reveals a druggable mechanism that leads to proteostasis reprogramming and facilitates KRASi resistance

A Renewable Tissue Resource of Phenotypically Stable, Biologically and Ethnically Diverse, Patient-Derived Human Breast Cancer Xenograft Models

Breast cancer research is hampered by difficulties in obtaining and studying primary human breast tissue, and by the lack of in vivo preclinical models that reflect patient tumor biology accurately. To overcome these limitations, we propagated a cohort of human breast tumors grown in the epithelium-free mammary fat pad of SCID/Beige and NOD/SCID/IL2γ-receptor null (NSG) mice, under a series of transplant conditions. Both models yielded stably transplantable xenografts at comparably high rates (~21% and ~19%, respectively). Of the conditions tested, xenograft take rate was highest in the presence of a low-dose estradiol pellet. Overall, 32 stably transplantable xenograft lines were established, representing 25 unique patients. Most tumors yielding xenografts were “triple-negative” (ER-PR-HER2+) (n=19). However, we established lines from three ER-PR-HER2+ tumors, one ER+PR-HER2−, one ER+PR+HER2− and one “triple-positive” (ER+PR+HER2+) tumor. Serially passaged xenografts show biological consistency with the tumor of origin, are phenotypically stable across multiple transplant generations at the histologic, transcriptomic, proteomic, and genomic levels, and show comparable treatment responses as those observed clinically. Xenografts representing 12 patients, including two ER+ lines, showed metastasis to the mouse lung. These models thus serve as a renewable, quality-controlled tissue resource for preclinical studies investigating treatment response and metastasis