Chlorophenyl-benzoxime inhibits pancreatic cancer cell proliferation, invasion and migration by down-regulating the expressions of interleukin-8 and cyclooxygenase-2

Purpose: To investigate the effects of chlorophenyl-benzoxime (CPBZX) on pancreatic cancer (PC) cell proliferation, invasion and migration, and the underlying mechanism of action. Methods: Pancreatic carcinoma cell lines (HuP-T4, HuP-T3 and BxPC-3) were cultured in Dulbecco's Modified Eagle medium (DMEM) containing 10 % fetal bovine serum (FBS), penicillin (100 U/mL) and streptomycin (10 μg/mL) at 37 ˚C in a humidified atmosphere containing 5 % CO2 and 95 % air. Cell proliferation was assessed using MTT assay. Real-time quantitative polymerase chain reaction (qRTPCR) and Western blotting were employed for the determination of changes in the levels of expression of carcinoembryonic antigen (CEA), interleukin-8 (IL-8) and cyclooxygenase-2 (COX 2). Cell invasion and migration were determined using Transwell and wound healing assays, respectively. Results: The results of MTT assay showed that CPBZX significantly and dose-dependently inhibited the proliferation of PC cells (p < 0.05). Incubation of HuP-T4 cells with CPBZX significantly and dosedependently reduced the invasive ability of the cells (p < 0.05). The migratory ability of HuP-T4 cells was also significantly and dose-dependently inhibited by CPBZX (p < 0.05). The results of Western blotting and qRT PCR showed that CPBZX treatment significantly and dose-dependently upregulated CEA mRNA expression (p < 0.05). On the other hand, the expressions of IL-8 and COX-2 were significantly and dose-dependently down-regulated by CPBZX. Treatment of pancreatic tumor mice with CPBZX significantly decreased tumor growth and metastasis of tumor cells to the pulmonary tissues, liver and lymph nodes (p < 0.05). Conclusion: The results of this study suggest that CPBZX inhibits the development and metastasis of PC via the down-regulation of IL-8 and COX 2 expressions, and therefore may find application in pancreatic cancer therapy

Reinforcement learning control for a robotic manipulator with unknown deadzone

In this paper, an actor critic neural network control is developed for a robotic manipulator. Both system uncertainties and unknown deadzone are considered in the tracking control design. Stability of the closed-loop system is analyzed via the Lyapunov’s direct method. The critic neural network is used to estimate the cost-to-go and the actor neural network is used to make the cost-to-go converge. Simulation studies are conducted to examine the effectiveness of the proposed actor critic neural network control

Epidemiology and risk factors of candidemia due to Candida parapsilosis in an intensive care unit

We analyzed the clinical features and risk factors of candidemia due to C. parapsilosis (n=104) in the intensive care unit of a tertiary hospital over six years. This was a monocentric, retrospective study of candidemia, conducted from January 2013 to March 2019. Epidemiological characteristics, clinical features, invasive procedures, laboratory data and outcomes of 267 patients with candidemia were analyzed to determine risk factors of candidemia due to C. parapsilosis. Sixty-three cases of C. albicans and 204 cases of non-C. albicans Candida (NCAC) species were included, the latter was composed of 104 cases of C. parapsilosis and 100 cases of non-C. albicans species (46 cases of C. tropicalis, 22 cases of C. glabrata, 23 cases of C. guilliermondii, 5 cases of C. krusei and 4 cases of C. lusitaniae), suggesting that C. parapsilosis was the predominant Candida species isolated from cases of candidemia. A binary multivariate logistic regression analysis showed that APACHE II scores, central venous catheterization and the use of broad-spectrum antibiotics were closely related to C. parapsilosis candidemia, with OR values of 1.159, 3.913 and 2.217, respectively. In conclusion, we found that C. parapsilosis was the main pathogen among the NCAC candidemia in the ICU patients. APACHE II scores, central venous catheterization and the use of broad-spectrum antibiotics were independent risk factors for the occurrence of C. parapsilosis candidemia, which may provide data to support the early introduction of anti-fungal therapy

De novo acute megakaryoblastic leukemia with p210 BCR/ABL and t(1;16) translocation but not t(9;22) Ph chromosome

Acute megakaryoblastic leukemia (AMKL) is a type of acute myeloid leukemia (AML), in which majority of the blasts are megakaryoblastic. De novo AMKL in adulthood is rare, and carries very poor prognosis. We here report a 45-year-old woman with de novo AMKL with BCR/ABL rearrangement and der(16)t(1;16)(q21;q23) translocation but negative for t(9;22) Ph chromosome. Upon induction chemotherapy consisting of homoharringtonine, cytarabine and daunorubicin, the patient achieved partial hematological remission. The patient was then switched to imatinib plus one cycle of CAG regimen (low-dose cytarabine and aclarubicin in combination with granulocyte colony-stimulating factor), and achieved complete remission (CR). The disease recurred after 40 days and the patient eventually died of infection. To the best of our knowledge, this is the first report of de novo AMKL with p210 BCR/ABL and der(16)t(1;16)(q21;q23) translocation but not t(9;22) Ph chromosome

Streaming Traffic Flow Prediction Based on Continuous Reinforcement Learning

Traffic flow prediction is an important part of smart transportation. The goal is to predict future traffic conditions based on historical data recorded by sensors and the traffic network. As the city continues to build, parts of the transportation network will be added or modified. How to accurately predict expanding and evolving long-term streaming networks is of great significance. To this end, we propose a new simulation-based criterion that considers teaching autonomous agents to mimic sensor patterns, planning their next visit based on the sensor's profile (e.g., traffic, speed, occupancy). The data recorded by the sensor is most accurate when the agent can perfectly simulate the sensor's activity pattern. We propose to formulate the problem as a continuous reinforcement learning task, where the agent is the next flow value predictor, the action is the next time-series flow value in the sensor, and the environment state is a dynamically fused representation of the sensor and transportation network. Actions taken by the agent change the environment, which in turn forces the agent's mode to update, while the agent further explores changes in the dynamic traffic network, which helps the agent predict its next visit more accurately. Therefore, we develop a strategy in which sensors and traffic networks update each other and incorporate temporal context to quantify state representations evolving over time

The Existence of Periodic Orbits and Invariant Tori for Some 3-Dimensional Quadratic Systems

We use the normal form theory, averaging method, and integral manifold theorem to study the existence of limit cycles in Lotka-Volterra systems and the existence of invariant tori in quadratic systems in ℝ3

GPT Understands, Too

While GPTs with traditional fine-tuning fail to achieve strong results on natural language understanding (NLU), we show that GPTs can be better than or comparable to similar-sized BERTs on NLU tasks with a novel method P-tuning -- which employs trainable continuous prompt embeddings. On the knowledge probing (LAMA) benchmark, the best GPT recovers 64\% (P@1) of world knowledge without any additional text provided during test time, which substantially improves the previous best by 20+ percentage points. On the SuperGlue benchmark, GPTs achieve comparable and sometimes better performance to similar-sized BERTs in supervised learning. Importantly, we find that P-tuning also improves BERTs' performance in both few-shot and supervised settings while largely reducing the need for prompt engineering. Consequently, P-tuning outperforms the state-of-the-art approaches on the few-shot SuperGlue benchmark

STAIBT: Blockchain and CP-ABE Empowered Secure and Trusted Agricultural IoT Blockchain Terminal

The integration of agricultural Internet of Things (IoT) and blockchain has become the key technology of precision agriculture. How to protect data privacy and security from data source is one of the difficult issues in agricultural IoT research. This work integrates cryptography, blockchain and Interplanetary File System (IPFS) technologies, and proposes a general IoT blockchain terminal system architecture, which strongly supports the integration of the IoT and blockchain technology. This research innovatively designed a fine-grained and flexible terminal data access control scheme based on the ciphertext-policy attribute-based encryption (CP-ABE) algorithm. Based on CP-ABE and DES algorithms, a hybrid data encryption scheme is designed to realize 1-to-N encrypted data sharing. A "horizontal + vertical" IoT data segmentation scheme under blockchain technology is proposed to realize the classified release of different types of data on the blockchain. The experimental results show that the design scheme can ensure data access control security, privacy data confidentiality, and data high-availability security. This solution significantly reduces the complexity of key management, can realize efficient sharing of encrypted data, flexibly set access control strategies, and has the ability to store large data files in the agricultural IoT