Traffic prediction has drawn increasing attention in AI research field due to the increasing availability of large-scale traffic data and its importance in the real world. For example, an accurate taxi demand prediction can assist taxi companies in pre-allocating taxis. The key challenge of traffic prediction lies in how to model the complex spatial dependencies and temporal dynamics. Although both factors have been considered in modeling, existing works make strong assumptions about spatial dependence and temporal dynamics, i.e., spatial dependence is stationary in time, and temporal dynamics is strictly periodical. However, in practice the spatial dependence could be dynamic (i.e., changing from time to time), and the temporal dynamics could have some perturbation from one period to another period. In this paper, we make two important observations: (1) the spatial dependencies between locations are dynamic; and (2) the temporal dependency follows daily and weekly pattern but it is not strictly periodic for its dynamic temporal shifting. To address these two issues, we propose a novel Spatial-Temporal Dynamic Network (STDN), in which a flow gating mechanism is introduced to learn the dynamic similarity between locations, and a periodically shifted attention mechanism is designed to handle long-term periodic temporal shifting. To the best of our knowledge, this is the first work that tackle both issues in a unified framework. Our experimental results on real-world traffic datasets verify the effectiveness of the proposed method.

Eleven thousand groundwater samples collected in the 2010s in an area of Marcellus shale-gas development are analyzed to assess spatial and temporal patterns of water quality. Using a new data mining technique, we confirm previous observations that methane concentrations in groundwater tend to be naturally elevated in valleys and near faults, but we also show that methane is also more concentrated near an anticline. Data mining also highlights waters with elevated methane that are not otherwise explained by geologic features. These slightly elevated concentrations occur near 7 out of the 1,385 shale-gas wells and near some conventional gas wells in the study area. For ten analytes for which uncensored data are abundant in this 3,000 km rural region, concentrations are unchanged or improved as compared to samples analyzed prior to 1990. Specifically, TDS, Fe, Mn, sulfate, and pH show small but statistically significant improvement, and As, Pb, Ba, Cl, and Na show no change. Evidence from this rural area could document improved groundwater quality caused by decreased acid rain (pH, sulfate) since the imposition of the Clean Air Act or decreased steel production (Fe, Mn). Such improvements have not been reported in groundwater in more developed areas of the U.S.

Breast cancer is the most frequently diagnosed tumor type and the primary leading cause of cancer deaths in women worldwide and multidrug resistance is the major obstacle for breast cancer treatment improvement. Emerging evidence suggests that metformin, the most widely used antidiabetic drug, resensitizes and cooperates with some anticancer drugs to exert anticancer effect. However, there are no data regarding the reversal effect of metformin on chemoresistance in breast cancer. In the present study, we investigated the resistance reversal effect of metformin on acquired multidrug-resistant breast cancer cells MCF-7/5-Fu derived from MCF-7 breast cancer cells and innate multidrug-resistant MDA-MB-231 breast cancer cells, and we found that metformin resensitized MCF7/5-FU and MDA-MB-231 to 5-fluorouracil (5-FU), adriamycin, and paclitaxel. We also observed that metformin reversed epithelial-mesenchymal transition (EMT) phenotype and decreased the invasive capacity of MCF7/5-FU and MDA-MB-231 cells. However, there were no significant changes upon metformin-treated MCF7 cells. Moreover, we found metformin treatment activated AMPK signal pathway in MCF7/5-FU and MDA-MB-231 cells and compound C, the AMPK inhibitor, could partly abolish the resensitization and EMT reversal effect of metformin. To the best of our knowledge, we are the first to report that metformin can resensitize multidrug-resistant breast cancer cells due to activating AMPK signal pathway. Our study will help elucidate the mechanism of chemoresistance and establish new strategies of chemotherapy for human breast cancer.

Traffic congestion plagues cities around the world. Recent years have witnessed an unprecedented trend in applying reinforcement learning for traffic signal control. However, the primary challenge is to control and coordinate traffic lights in large-scale urban networks. No one has ever tested RL models on a network of more than a thousand traffic lights. In this paper, we tackle the problem of multi-intersection traffic signal control, especially for large-scale networks, based on RL techniques and transportation theories. This problem is quite difficult because there are challenges such as scalability, signal coordination, data feasibility, etc. To address these challenges, we (1) design our RL agents utilizing ‘pressure’ concept to achieve signal coordination in region-level; (2) show that implicit coordination could be achieved by individual control agents with well-crafted reward design thus reducing the dimensionality; and (3) conduct extensive experiments on multiple scenarios, including a real-world scenario with 2510 traffic lights in Manhattan, New York City 1 2.

SET oncoprotein is an endogenous inhibitor of protein phosphatase 2A (PP2A), and SET-mediated PP2A inhibition is an important regulatory mechanism for promoting cancer initiation and progression of several types of human leukemia disease. However, its potential relevance in solid tumors as non-small cell lung cancer (NSCLC) remains mostly unknown. In this study, we showed that SET was evidently overexpressed in human NSCLC cell lines and NSCLC tissues. Clinicopathologic analysis showed that SET expression was significantly correlated with clinical stage (p < 0.001), and lymph node metastasis (p < 0.05). Kaplan-Meier analysis revealed that patients with high SET expression had poorer overall survival rates than those with low SET expression. Moreover, knockdown of SET in NSCLC cells resulted in attenuated proliferative and invasive abilities. The biological effect of SET on proliferation and invasion was mediated by the inhibition of the PP2A, which in turn, activation of AKT and ERK, increased the expression of cyclin D1 and MMP9, and decreased the expression of p27. Furthermore, we observed that restoration of PP2A using SET antagonist FTY720 impaired proliferative and invasive potential in vitro, as well as inhibited tumor growth in vivo of NSCLC cells. Taken together, SET oncoprotein plays an important role in NSCLC progression, which could serve as a potential prognosis marker and a novel therapeutic target for NSCLC patients.

Breast cancer is a heterogeneous disease, and triple-negative breast cancer (TNBC) continues to be a serious health problem. The potential involvement of lncRNAs in TNBC progression remains unexplored. Here, we demonstrated that LINC01638 is highly expressed in TNBC tissues and cells. LINC01638 maintains the mesenchymal traits of TNBC cells, including an enriched epithelial-mesenchymal transition (EMT) signature and cancer stem cell-like state. LINC01638 knockdown suppresses tumor proliferation and metastasis both in vitro and in vivo. LINC01638 overexpression predicts a poor outcome of breast cancer patients. Mechanistically, LINC01638 interacts with c-Myc to prevent SPOP-mediated c-Myc ubiquitination and degradation. C-Myc transcriptionally enhances MTDH (metadherin) expression and subsequently activates Twist1 expression to induce EMT. Our findings describe LINC01638-mediated signal transduction and highlight the crucial role of LINC01638 in TNBC progression.

Lactate dehydrogenase A (LDHA) is involved in a variety of cancers. The purpose of this study was to investigate the expression, prognostic roles and function of LDHA in breast cancer. We found that LDHA was upregulated in both breast cancer cell lines and clinical specimens using quantitative real-time PCR (qRT-PCR). Immunohistochemistry (IHC) analysis of tissue microarrays (TMAs) showed that high LDHA expression was associated with cell proliferation, metastasis and poor patient overall survival (OS) and disease free survival (DFS). Furthermore, we found that LDHA promoted glycolysis and cell proliferation in vitro and in vivo. We also performed luciferase reporter assays and found that LDHA was a direct target of miR-34a. Repression of LDHA by miR-34a suppressed glycolysis and cell proliferation in breast cancer cells in vitro. Our findings provide clues regarding the role of miR-34a as a tumor suppressor in breast cancer through the inhibition of LDHA both in vitro and in vivo. Targeting LDHA through miR-34a could be a potential therapeutic strategy in breast cancer.

Recently, emerging evidence has suggested that long noncoding RNAs (lncRNAs) have crucial roles in cancer progression. Here, we demonstrated that the lncRNA MIR4435-2HG was highly expressed in lung cancer tissues and correlated with histological grades and lymph node metastasis. Phenotypic analysis indicated that MIR4435-2HG knockdown inhibited lung cancer cell proliferation and invasion in vitro and in vivo. Notably, MIR4435-2HG knockdown suppressed the EMT (epithelial-mesenchymal transition) process and cancer stem cell traits of lung cancer cells. Mechanistically, MIR4435-2HG knockdown decreased the transactivation of β-catenin. MIR4435-2HG interacted with β-catenin and thus prevented its degradation by the proteasome system. Our findings highlight the important roles and mechanisms of MIR4435-2HG in lung cancer progression. High expression of lncRNA MIR4435-2HG correlates with lung cancer progression MIR4435-2HG promotes lung cancer cells proliferation and invasion MIR4435-2HG knockdown suppresses the EMT process and cancer stem cell traits MIR4435-2HG knockdown inhibits the β-catenin signalling.