Design and Testing of Real-Time Sensing System Used in Predicting the Leakage of Subsea Pipeline.

This study integrates the array sensing module and the flow leakage algorithm. In this study, a real-time monitoring deep-sea pipeline damage sensing system is designed to provide decision-making parameters such as damage coordinates and damage area. The array sensor module is composed of multiple YF-S201 hall sensors and controllers. YF-S201 hall sensors are arranged inside the pipeline in an array. The flow signal in the deep-sea pipeline can be transmitted to the electronic control interface to analyze the leakage position and leakage flowrate of the pipeline. The theory of this system is based on the conservation of mass. Through the flow of each sensor, it is judged whether the pipeline is damaged. When the pipeline is not damaged, the flowrate of each sensor is almost the same. When the pipeline is damaged, the flowrate will drop significantly. When the actual size of leakage in the pipeline is 5.28 cm2, the size calculated by the flowrate of hall sensors is 2.58 cm2 in average, indicating the error between experimental data and theoretical data is 46%. When the actual size of leakage in the pipeline is 1.98 cm2, the size calculated by the flowrate of hall sensors is 1.31 cm2 in average, indicating the error between experimental data and theoretical data is 21%. This can accurately confirm the location of the broken pipeline, which is between sensor A and sensor B, so that the AUV/ROV can accurately locate and perform pipeline maintenance in real time. It is expected to be able to monitor the flowrate through the array magnetic sensing module designed in this study. It can grasp the status of deep-sea pipelines, improve the quality of deep-sea extraction and pipeline maintenance speed.

A Self-Powered Strain Sensor Applied to Real-Time Monitoring for Movable Structures.

This study uses near-field electrospinning (NFES) technology to make a novel self-powered strain sensor and applies it to the real-time monitoring of a bending structure, so that the measurement equipment can be reduced in volume. A self-powered strain sensor consists of polyvinylidene difluoride (PVDF) fibers, a PDMS fixed substrate, and an aluminum electrode. PVDF fibers are spun with DMSO and acetone using NFES technology, with a diameter of about 8 µm, Young's modulus of 1.1 GPa, and piezoelectric effect of up to 230 mV. The fixed substrate is a film made of PDMS by thermal curing, then adhered to the PDMS film surface of the sheet Al metal as an Al electrode, and then combined with PVDF fiber film, to become a self-powered strain sensor. As a result, the XRD ß value of the self-powered strain sensor reaches 2112 and the sensitivity is increased by 20% over a traditional strain sensor. The cumulative angle algorithm can be applied to measure the angular change of the object over a unit of time or the cumulative displacement of the object over the entire period of motion. The experimental results demonstrate that the self-powered strain sensor combined with the angle accumulation algorithm may be applied to monitor the bending structure, thereby achieving continuous measurements of bending structure changes, and improving on traditional piezoelectric sensors, which can only be sensed once. In the future, self-powered strain sensors will have the ability to continuously measure in real-time, enabling the use of piezoelectric sensors for long-term monitoring of structural techniques.

New Insights Into Drug Repurposing for COVID-19 Using Deep Learning.

The coronavirus disease 2019 (COVID-19) has continued to spread worldwide since late 2019. To expedite the process of providing treatment to those who have contracted the disease and to ensure the accessibility of effective drugs, numerous strategies have been implemented to find potential anti-COVID-19 drugs in a short span of time. Motivated by this critical global challenge, in this review, we detail approaches that have been used for drug repurposing for COVID-19 and suggest improvements to the existing deep learning (DL) approach to identify and repurpose drugs to treat this complex disease. By optimizing hyperparameter settings, deploying suitable activation functions, and designing optimization algorithms, the improved DL approach will be able to perform feature extraction from quality big data, turning the traditional DL approach, referred to as a "black box," which generalizes and learns the transmitted data, into a "glass box" that will have the interpretability of its rationale while maintaining a high level of prediction accuracy. When adopted for drug repurposing for COVID-19, this improved approach will create a new generation of DL approaches that can establish a cause and effect relationship as to why the repurposed drugs are suitable for treating COVID-19. Its ability can also be extended to repurpose drugs for other complex diseases, develop appropriate treatment strategies for new diseases, and provide precision medical treatment to patients, thus paving the way to discover new drugs that can potentially be effective for treating COVID-19.

Prediction of drug adverse events using deep learning in pharmaceutical discovery.

Traditional machine learning methods used to detect the side effects of drugs pose significant challenges as feature engineering processes are labor-intensive, expert-dependent, time-consuming and cost-ineffective. Moreover, these methods only focus on detecting the association between drugs and their side effects or classifying drug-drug interaction. Motivated by technological advancements and the availability of big data, we provide a review on the detection and classification of side effects using deep learning approaches. It is shown that the effective integration of heterogeneous, multidimensional drug data sources, together with the innovative deployment of deep learning approaches, helps reduce or prevent the occurrence of adverse drug reactions (ADRs). Deep learning approaches can also be exploited to find replacements for drugs which have side effects or help to diversify the utilization of drugs through drug repurposing.

Machine learning on adverse drug reactions for pharmacovigilance.

Machine learning, especially deep learning, has the predictive power to predict adverse drug reactions, repurpose drugs and perform precision medicine. We provide a background of machine learning and propose a potential high-performance deep learning framework for its successful applications in these practices.

Extending the Lifespan of Soluble Lead Flow Batteries with a Sodium Acetate Additive.

In this report, we present a method for the construction of a soluble lead flow battery (SLFB) with an extended cycle life. By supplying an adequate amount of sodium acetate (NaOAc) to the electrolyte, a cycle life extension of over 50% is demonstrated for SLFBs via long-term galvanostatic charge/discharge experiments. A higher quality of the PbO2 electrodeposit at the positive electrode is quantitatively validated for NaOAc-added electrolyte by throwing index (TI) measurements. Images acquired by scanning electron microscopy (SEM) also exhibit more integrated PbO2 surface morphology when the SLFB is operated with the NaOAc-added electrolyte. This work indicates that electrolyte modification can be a plausible route to economically enable SLFBs for large-scale energy storage.

Ag8SnS6: a new IR solar absorber material with a near optimal bandgap.

We report the synthesis and photovoltaic properties of a new ternary solar absorber - Ag8SnS6 nanocrystals prepared by successive ionic layer adsorption reaction (SILAR) technique. The synthesized Ag8SnS6 nanocrystals have a bandgap E g of 1.24-1.41 eV as revealed from UV-Vis and external quantum efficiency (EQE) measurements. Its photovoltaic properties were characterized by assembling a liquid-junction Ag8SnS6 sensitized solar cell for the first time. The best cell yielded a J sc of 9.29 mA cm-2, a V oc of 0.23 V, an FF of 31.3% and a power conversion efficiency (PCE) of 0.64% under 100% incident light illumination using polysulfide electrolyte and Au counter electrode. The efficiency improved to 1.43% at a reduced light intensity of 10% sun. When the polysulfide was replaced by a cobalt electrolyte with a lower redox level, the V oc increased to 0.54 V and PCE increased to 2.29% under 0.1 sun, a respectable efficiency for a new solar material. The EQE spectrum covers the spectral range of 300-1000 nm with a maximum EQE of 77% at λ = 600 nm. The near optimal E g and the respectable photovoltaic performance suggest that Ag8SnS6 nanocrystals have potential to be an efficient IR solar absorber.

Post-injury treatment with 7,8-dihydroxyflavone, a TrkB receptor agonist, protects against experimental traumatic brain injury via PI3K/Akt signaling.

Tropomyosin-related kinase B (TrkB) signaling is critical for promoting neuronal survival following brain damage. The present study investigated the effects and underlying mechanisms of TrkB activation by the TrkB agonist 7,8-dihydroxyflavone (7,8-DHF) on traumatic brain injury (TBI). Mice subjected to controlled cortical impact received intraperitoneal 7,8-DHF or vehicle injection 10 min post-injury and subsequently daily for 3 days. Behavioral studies, histology analysis and brain water content assessment were performed. Levels of TrkB signaling-related molecules and apoptosis-related proteins were analyzed. The protective effect of 7,8-DHF was also investigated in primary neurons subjected to stretch injury. Treatment with 20 mg/kg 7,8-DHF attenuated functional deficits and brain damage up to post-injury day 28. 7,8-DHF also reduced brain edema, neuronal death, and apoptosis at day 4. These changes were accompanied by a significant decrease in cleaved caspase-3 and increase in Bcl-2/Bax ratio. 7,8-DHF enhanced phosphorylation of TrkB, Akt (Ser473/Thr308), and Bad at day 4, but had no effect on Erk 1/2 phosphorylation. Moreover, 7,8-DHF increased brain-derived neurotrophic factor levels and promoted cAMP response element-binding protein (CREB) activation. This beneficial effect was attenuated by inhibition of TrkB or PI3K/Akt. 7,8-DHF also promoted survival and reduced apoptosis in cortical neurons subjected to stretch injury. Remarkably, delayed administration of 7,8-DHF at 3 h post-injury reduced brain tissue damage. Our study demonstrates that activation of TrkB signaling by 7,8-DHF protects against TBI via the PI3K/Akt but not Erk pathway, and this protective effect may be amplified via the PI3K/Akt-CREB cascades.

Wogonin improves histological and functional outcomes, and reduces activation of TLR4/NF-κB signaling after experimental traumatic brain injury.

BACKGROUND: Traumatic brain injury (TBI) initiates a neuroinflammatory cascade that contributes to neuronal damage and behavioral impairment. This study was undertaken to investigate the effects of wogonin, a flavonoid with potent anti-inflammatory properties, on functional and histological outcomes, brain edema, and toll-like receptor 4 (TLR4)- and nuclear factor kappa B (NF-κB)-related signaling pathways in mice following TBI. METHODOLOGY/PRINCIPAL FINDINGS: Mice subjected to controlled cortical impact injury were injected with wogonin (20, 40, or 50 mg·kg(-1)) or vehicle 10 min after injury. Behavioral studies, histology analysis, and measurement of blood-brain barrier (BBB) permeability and brain water content were carried out to assess the effects of wogonin. Levels of TLR4/NF-κB-related inflammatory mediators were also examined. Treatment with 40 mg·kg(-1) wogonin significantly improved functional recovery and reduced contusion volumes up to post-injury day 28. Wogonin also significantly reduced neuronal death, BBB permeability, and brain edema beginning at day 1. These changes were associated with a marked reduction in leukocyte infiltration, microglial activation, TLR4 expression, NF-κB translocation to nucleus and its DNA binding activity, matrix metalloproteinase-9 activity, and expression of inflammatory mediators, including interleukin-1ß, interleukin-6, macrophage inflammatory protein-2, and cyclooxygenase-2. CONCLUSIONS/SIGNIFICANCE: Our results show that post-injury wogonin treatment improved long-term functional and histological outcomes, reduced brain edema, and attenuated the TLR4/NF-κB-mediated inflammatory response in mouse TBI. The neuroprotective effects of wogonin may be related to modulation of the TLR4/NF-κB signaling pathway.

Correction: Wogonin Improves Histological and Functional Outcomes, and Reduces Activation of TLR4/NF-κB Signaling after Experimental Traumatic Brain Injury.

[This corrects the article DOI: 10.1371/journal.pone.0030294.].