Acute stress induces tissue damage through excessive oxidative stress. Dexmedetomidine (DEX) reportedly has an antioxidant effect. However, protective roles and related potential molecular mechanisms of DEX against kidney injury induced by acute stress are unknown. Herein, rats were forced to swim 15 min followed by restraint stress for 3 h with/without DEX (30 μg/kg). Successful model establishment was validated by an open-field test. Assessment of renal function (creatinine, urea nitrogen), histopathology, oxidative stress (malondialdehyde, glutathione, and superoxide dismutase), and apoptosis (transferase-mediated dUTP nick end labeling) was performed. Localization of apoptosis was determined by immunohistochemistry of cleaved caspase 3 protein. In addition, key proteins of the death receptor-mediated pathway, mitochondrial pathway, endoplasmic reticulum stress (ERS) pathway, and ROS/JNK signaling pathway were measured by Western blot. We found that DEX significantly improved renal dysfunction, ameliorated kidney injury, reduced oxidative stress, and alleviated apoptosis. DEX also inhibited the release of norepinephrine (NE), decreased the production of reactive oxygen species (ROS), and inhibited JNK phosphorylation. Additionally, DEX downregulated the expression of Bax, cytochrome C, cleaved caspase 9, and cleaved caspase 3 proteins in mitochondria-dependent pathways. In summary, DEX protects against acute stress-induced kidney injury in rats by reducing oxidative stress and apoptosis via inhibition of the ROS/JNK pathway.

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Acute stress is a frequent and unpredictable disease for many animals. Stress is widely considered to affect liver function. However, the underlying mechanism by which dexmedetomidine (DEX) attenuates acute stress‐induced liver injury in rats remains unclear. In this study, we used forced swimming for 15 min and acute 3‐hr restraint stress model. Behavioral tests and changes in norepinephrine levels confirmed the successful establishment of the acute stress model. Acute stress‐induced liver injury, evidenced by hematoxylin and eosin‐stained pathological sections and increased serum aminotransferase and aspartate aminotransferase levels, was reduced in DEX‐treated livers. Reactive oxygen species and oxidative stress levels were dramatically decreased with DEX treatment compared with acute stress‐induced liver injury. DEX significantly reduced acute stress‐induced liver inflammation and apoptosis, as assessed by terminal deoxynucleotidyl transferase dUTP nick‐end labeling staining and inflammation and apoptosis‐related protein levels. DEX treatment also effectively inhibited acute stress‐induced c‐Jun N‐terminal kinase (JNK), P38, and BAD signaling pathway activation, and significantly induced MKP‐1 activation. Thus, DEX has a protective effect on acute‐stress‐induced liver injury by reducing inflammation and apoptosis, which suggests a potential clinical application for DEX in stress syndrome.

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Speech evaluation is an essential component in computerassisted language learning (CALL). While speech evaluation on English has been popular, automatic speech scoring on low resource languages remains challenging. Work in this area has focused on monolingual specific designs and handcrafted features stemming from resource-rich languages like English. Such approaches are often difficult to generalize to other languages, especially if we also want to consider suprasegmental qualities such as rhythm. In this work, we examine three different languages that possess distinct rhythm patterns: English (stresstimed), Malay (syllable-timed), and Tamil (mora-timed). We exploit robust feature representations inspired by music processing and vector representation learning. Empirical validations show consistent gains for all three languages when predicting pronunciation, rhythm and intonation performance.

Industry 4.0 focuses on continuous interconnection services, allowing for the continuous and uninterrupted exchange of signals or information between related parties. The application of messaging protocols for transferring data to remote locations must meet specific specifications such as asynchronous communication, compact messaging, operating in conditions of unstable connection of the transmission line of data, limited network bandwidth operation, support multilevel Quality of Service (QoS), and easy integration of new devices. The Message Queue Telemetry Transport (MQTT) protocol is used in software applications that require asynchronous communication. It is a light and simplified protocol based on publish-subscribe messaging and is placed functionally over the TCP/IP protocol. It is designed to minimize the required communication bandwidth and system requirements increasing reliability and probability of successful message transmission, making it ideal for use in Machine-to-Machine (M2M) communication or networks where bandwidth is limited, delays are long, coverage is not reliable, and energy consumption should be as low as possible. Despite the fact that the advantage that MQTT offers its way of operating does not provide a serious level of security in how to achieve its interconnection, as it does not require protocol dependence on one intermediate third entity, the interface is dependent on each application. This paper presents an innovative real-time anomaly detection system to detect MQTT-based attacks in cyber-physical systems. This is an online-semisupervised learning neural system based on a small number of sampled patterns that identify crowd anomalies in the MQTT protocol related to specialized attacks to undermine cyber-physical systems.