Objective Unidentified mechanisms largely restrict the viability of effective therapies in pharmacoresistant epilepsy. Our previous study revealed that hyperactivity of the subiculum is crucial for the genesis of pharmacoresistance in temporal lobe epilepsy (TLE), but the underlying molecular mechanism is not clear. Methods Here, we examined the role of subicular caspase‐1, a key neural pro‐inflammatory enzyme, in pharmacoresistant TLE. Results We found that the expression of activated caspase‐1 in the subiculum, but not the CA1, was upregulated in pharmacoresistant amygdaloid‐kindled rats. Early overexpression of caspase‐1 in the subiculum was sufficient to induce pharmacoresistant TLE in rats, whereas genetic ablation of caspase‐1 interfered with the genesis of pharmacoresistant TLE in both kindled rats and kainic acid‐treated mice. The pro‐pharmacoresistance effect of subicular caspase‐1 was mediated by its downstream inflammasome‐dependent interleukin‐1β. Further electrophysiological results showed that inhibiting caspase‐1 decreased the excitability of subicular pyramidal neurons through influencing the excitation/inhibition balance of presynaptic input. Importantly, a small molecular caspase‐1 inhibitor CZL80 attenuated seizures in pharmacoresistant TLE models, and decreased the neuronal excitability in the brain slices obtained from patients with pharmacoresistant TLE. Interpretation These results support the subicular caspase‐1‐interleukin‐1β inflammatory pathway as a novel alternative mechanism hypothesis for pharmacoresistant TLE, and present caspase‐1 as a potential target. ANN NEUROL 2021;90:377–390

Herein, the sulfonyl fluoro isocyanides were first developed as a new type of SuFExable synthon, and they are used as building blocks in the Ugi reaction (U-4CR). The Ugi reaction was established and the substrate scope was investigated, and various sulfonyl fluoro α-amino amides and peptides could be reached in a one-step synthesis. Therefore, this protocol opens a new vision for SuFExable building blocks and click chemistry, and it also provides a distinct approach to sulfonyl fluoro peptides.

A multicomponent reaction for the synthesis of β‑aminoxy amides is described. In this reaction, N-hydroxamic acids, yna-mides and aldehydes could assemble efficiently to deliver structurally diverse β‑aminoxy amides under the...

Numerous internal and external intrusion attacks have appeared one after another, which has become a major problem affecting the normal operation of the power system. The power system is the infrastructure of the national economy, ensuring that the information security of its network not only is an aspect of computer information security but also must consider high-standard security requirements. This paper analyzes the intrusion threat brought by the power information network and conducts in-depth research and investigation combined with the intrusion detection technology of the power information network. It analyzes the structure of the power knowledge network and cloud computing through deep learning-based methods and provides a network interference detection model. The model combines the methods of abuse detection and anomaly detection, which solves the problem that the abuse analysis model does not detect new attack variants. At the same time, for big data network data retrieval, it retrieves and analyzes data flow quickly and accurately with the help of deep learning of data components. It uses a fuzzy integral method to optimize the accuracy of power information network intrusion prediction, and the accuracy reaches 98.11%, with an increase of 0.6%.

The power grid is an important connection between power sources and users, responsible for supplying and distributing electric energy to users. Modern power grids are widely distributed and large in scale, and their security faces new problems and challenges. Information entropy theory is an objective weighting method that compares the information order of each evaluation index to judge the weight value. With the wide application of entropy theory in various disciplines, the subject of introducing entropy into the power system has been gradually concerned. This article aims to study the smart terminal security technology of the power grid perception layer based on information entropy data mining. This article analyzes its related methods and designs a smart terminal for the power grid. On this basis, a data analysis platform is built and a safety plan is designed. The result is that the average absolute error, root mean square error, average absolute percentage error, and mean square error of the platform's power load forecast are 1.58, 1.96, 8.2%, and 3.93, respectively. These error values are within the ideal range, and the data processing ability is strong. The packet loss rate of the adversary's eavesdropping was tested, and the average packet loss rates at locations a, b, c, and d were 1.05, 1.2, 1.81, and 2.2%, respectively. Data packets will be definitely lost, so the platform is highly secure.