GOLVEN peptide signalling through RGI receptors and MPK6 restricts asymmetric cell division during lateral root initiation

During lateral root initiation, lateral root founder cells undergo asymmetric cell divisions that generate daughter cells with different sizes and fates, a prerequisite for correct primordium organogenesis. An excess of the GLV6/RGF8 peptide disrupts these initial asymmetric cell divisions, resulting in more symmetric divisions and the failure to achieve lateral root organogenesis. Here, we show that loss-of-function GLV6 and its homologue GLV10 increase asymmetric cell divisions during lateral root initiation, and we identified three members of the RGF1 INSENSITIVE/RGF1 receptor subfamily as likely GLV receptors in this process. Through a suppressor screen, we found that MITOGEN-ACTIVATED PROTEIN KINASE6 is a downstream regulator of the GLV pathway. Our data indicate that GLV6 and GLV10 act as inhibitors of asymmetric cell divisions and signal through RGF1 INSENSITIVE receptors and MITOGEN-ACTIVATED PROTEIN KINASE6 to restrict the number of initial asymmetric cell divisions that take place during lateral root initiation. The authors demonstrate the negative role of GOLVEN peptides during lateral root initiation in Arabidopsis, at the very early stage of the first asymmetric cell division of lateral root founder cells, and identify the receptors for these peptides

Conditional anonymous remote healthcare data sharing over blockchain

As an important carrier of healthcare data, Electronic Medical Records (EMRs) generated from various sensors, i.e., wearable, implantable, are extremely valuable research materials for artificial intelligence and machine learning. The efficient circulation of EMRs can improve remote medical services and promote the development of the related healthcare industry. However, in traditional centralized data sharing architectures, the balance between privacy and traceability still cannot be well handled. To address the issue that malicious users cannot be locked in the fully anonymous sharing schemes, we propose a trackable anonymous remote healthcare data storing and sharing scheme over decentralized consortium blockchain. Through an “on-chain & off-chain” model, it relieves the massive data storage pressure of medical blockchain. By introducing an improved proxy re-encryption mechanism, the proposed scheme realizes the fine-gained access control of the outsourced data, and can also prevent the collusion between semi-trusted cloud servers and data requestors who try to reveal EMRs without authorization. Compared with the existing schemes, our solution can provide a lower computational overhead in repeated EMRs sharing, resulting in a more efficient overall performance

Unprecedented Ambient Sulfur Trioxide (SO3) Detection : Possible Formation Mechanism and Atmospheric Implications

Sulfur trioxide (SO3) is a crucial compound for atmospheric sulfuric acid (H2SO4) formation, acid rain formation, and other atmospheric physicochemical processes. During the daytime, SO3 is mainly produced from the photo-oxidation of SO2 by OH radicals. However, the sources of SO3 during the early morning and night, when OH radicals are scarce, are not fully understood. We report results from two field measurements in urban Beijing during winter and summer 2019, using a nitrate-CI-APi-LTOF (chemical ionization-atmospheric pressure interface-long-time-offlight) mass spectrometer to detect atmospheric SO3 and H2SO4. Our results show the level of SO3 was higher during the winter than during the summer, with high SO3 levels observed especially during the early morning (similar to 05:00 to similar to 08:30) and night (similar to 18:00 to similar to 05:00 the next day). On the basis of analysis of SO2, NOx, black carbon, traffic flow, and atmospheric ions, we suggest SO3 could be formed from the catalytic oxidation of SO2 on the surface of traffic-related black carbon. This previously unidentified SO3 source results in significant H2SO4 formation in the early morning and thus promotes sub-2.5 nm particle formation. These findings will help in understanding urban SO3 and formulating policies to mitigate secondary particle formation in Chinese megacities.Peer reviewe

A Novel Base-Station Selection Strategy for Cellular Vehicle-to-Everything (C-V2X) Communications

Cellular vehicle-to-everything (C-V2X) communication facilitates the improved safety, comfort, and efficiency of vehicles and mobility by exchanging information between vehicles and other entities. In general, only the macrocell or only the femtocell is the communication infrastructure for C-V2X. Currently, a macro-femtocell network is used as the new C-V2X networking architecture. However, there are two unresolved problems for C-V2X in macro-femtocell networks. Firstly, vehicle mobility requires the frequent switching of connections between different base stations; invalid switching results in worse communication quality. Secondly, unintelligent base station selections cause network congestion and network-load imbalance. To address the above challenges, this paper proposes a base station selection strategy based on a Markov decision policy for a vehicle in a macro-femtocell system. Firstly, we present a mechanism to predict received signal strength (RSS) for base station selection. Secondly, a comparing Markov decision policy algorithm is presented in C-V2X. To the best of our knowledge, this is the first attempt to achieve predicted RSS based on a Markov decision policy in C-V2X technology. To validate the proposed mechanism, we simulated the traditional base station selection and our proposal when the vehicle moved at different speeds. This demonstrates that the effectiveness of a traditional base station selection policy is obvious only at high speeds, and this weakness can be resolved by our proposal. Then, we compare our solution with the traditional base station selection policy. The simulation results show that our solution is effective at switching connections between base stations, and it can effectively prevent the overloading of network resources

Integrated Deep Neural Networks-Based Complex System for Urban Water Management

Although the management and planning of water resources are extremely significant to human development, the complexity of implementation is unimaginable. To achieve this, the high-precision water consumption prediction is actually the key component of urban water optimization management system. Water consumption is usually affected by many factors, such as weather, economy, and water prices. If these impact factors are directly combined to predict water consumption, the weight of each perspective on the water consumption will be ignored, which will be greatly detrimental to the prediction accuracy. Therefore, this paper proposes a deep neural network-based complex system for urban water management. The essence of it is to formulate a water consumption prediction model with the aid of principal component analysis (PCA) and the integrated deep neural network, which is abbreviated as UWM-Id. The PCA classifies the factors affecting water consumption in the original data into three categories according to their correlation and inputs them into the neural network model. The results in the previous step are assigned weights and integrated into the form of fully connected layer. Finally, analyzing the sensitivity of the proposed UWM-Id and comparing its performance with a series of commonly used baseline methods for data mining, a large number of experiments have proved that UWM-Id has good performance and can be used for urban water management system

Challenges in Recycling Spent Lithium-Ion Batteries: Spotlight on Polyvinylidene Fluoride Removal

In the recycling of retired lithium-ion batteries (LIBs), the cathode materials containing valuable metals should be first separated from the current collector aluminum foil to decrease the difficulty and complexity in the subsequent metal extraction. However, strong the binding force of organic binder polyvinylidene fluoride (PVDF) prevents effective separation of cathode materials and Al foil, thus affecting metal recycling. This paper reviews the composition, property, function, and binding mechanism of PVDF, and elaborates on the separation technologies of cathode material and Al foil (e.g., physical separation, solid-phase thermochemistry, solution chemistry, and solvent chemistry) as well as the corresponding reaction behavior and transformation mechanisms of PVDF. Due to the characteristic variation of the reaction systems, the dissolution, swelling, melting, and degradation processes and mechanisms of PVDF exhibit considerable differences, posing new challenges to efficient recycling of spent LIBs worldwide. It is critical to separate cathode materials and Al foil and recycle PVDF to reduce environmental risks from the recovery of retired LIBs resources. Developing fluorine-free alternative materials and solid-state electrolytes is a potential way to mitigate PVDF pollution in the recycling of spent LIBs in the EV era

Study on the Comprehensive Utilization of Bitter Almond Shell

A comprehensive process was developed to make full use of the solid and liquid products during the production of activated carbon. Almond shell waste was modified with phosphoric acid and thermally treated to give activated carbon. Wood vinegar was generated and collected within the temperature range of 90 to 500 °C, and the maximum amount of the wood vinegar was in the range of 170 to 370 °C, which also gave the strongest anti-pathogens activities with the lowest pH and the highest organic acid content. The remaining residue after wood vinegar generation was further calcined in inert atmosphere to obtain high surface area activated carbon. The pre-treatment of almond shell with H3PO4 leads to the higher surface area, but H3PO4 solution with concentration more than 40% does not increase the surface area further. The impregnation of H3PO4 helps the formation of pores in the almond shell during the calcination, and gives higher iodine number and methylene blue sorption capacity of the resultant activated carbon materials

Andrographolide Induces Autophagic Cell Death and Inhibits Invasion and Metastasis of Human Osteosarcoma Cells in An Autophagy-Dependent Manner

Background/Aims: Osteosarcoma (OS) is the most common primary malignant tumor of bone tissue. Although treatment effectiveness has improved, the OS survival rate has fluctuated in recent years. Andrographolide (AG) has been reported to have antitumor activity against a variety of tumors. Our aim was to investigate the effects and potential mechanisms of AG in human osteosarcoma. Methods: Cell viability and morphological changes were assessed by MTT and live/dead assays. Apoptosis was detected using Annexin V-FITC/PI double staining, DAPI, and caspase-3 assays. Autophagy was detected with mRFP-GFP-LC3 adenovirus transfection and western blot. Cell migration and invasion were detected by wound healing assay and Transwell® experiments. Results: AG dose-dependently reduced the viability of osteosarcoma cells. No increase in apoptosis was detected in AG-treated human OS MG-63 and U-2OS cells, and the pan-caspase inhibitor z-VAD did not attenuate AG-induced cell death. However, AG induced autophagy by suppressing PI3K/Akt/mTOR and enhancing JNK signaling pathways. 3-MA and Beclin-1 siRNA could reverse the cytotoxic effects of AG. In addition, AG inhibited the invasion and metastasis of OS, and this effect could be reversed with Beclin-1 siRNA. Conclusion: AG inhibits viability and induces autophagic death in OS cells. AG-induced autophagy inhibits the invasion and metastasis of OS