Mechanism and deterioration pattern of sandstone surrounding rock voiding at bottom of heavy-haul railway tunnel.

This study combines laboratory experiments and discrete element simulation methods to analyze the mechanism and deterioration patterns of sandstone surrounding rock voiding the bottom of a heavy-haul railway tunnel. It is based on previously acquired measurement data from optical fiber grating sensors installed in the Taihangshan Mountain Tunnel of the Wari Railway. By incorporating rock particle wastage rate results, a method for calculating the peak strength and elastic modulus attenuation of surrounding rock is proposed. Research indicates that the operation of heavy-haul trains leads to an instantaneous increase in the dynamic water pressure on the bottom rock ranging 144.4-390.0%, resulting in high-speed water flow eroding the rock. After 1-2 years of operation, the bottom water and soil pressures increase by 526.5% and 390.0%, respectively. Focusing on sandstone surrounding rock with high observability, laboratory experiments were conducted to monitor the degradation stages of infiltration, particle loss, and voiding of rock under the action of dynamic water flow. The impact of water flow on the "cone-shaped" bottom rock deformation was also clarified. The extent of rock deterioration and voiding was determined using miniature water and soil pressure sensors in conjunction with discrete element numerical simulations. The measured rock particle loss was used as a criterion. Finally, a fitting approach is derived to calculate the peak strength and elastic modulus attenuation of surrounding rock, gaining insight into and providing a reference for the maintenance and disposal measures for the bottom operation of heavy-haul railway tunnels.

Discovery of DNL343: A Potent, Selective, and Brain-Penetrant eIF2B Activator Designed for the Treatment of Neurodegenerative Diseases.

Eukaryotic translation initiation factor 2B (eIF2B) is a key component of the integrated stress response (ISR), which regulates protein synthesis and stress granule formation in response to cellular insult. Modulation of the ISR has been proposed as a therapeutic strategy for treatment of neurodegenerative diseases such as vanishing white matter (VWM) disease and amyotrophic lateral sclerosis (ALS) based on its ability to improve cellular homeostasis and prevent neuronal degeneration. Herein, we report the small-molecule discovery campaign that identified potent, selective, and CNS-penetrant eIF2B activators using both structure- and ligand-based drug design. These discovery efforts culminated in the identification of DNL343, which demonstrated a desirable preclinical drug profile, including a long half-life and high oral bioavailability across preclinical species. DNL343 was progressed into clinical studies and is currently undergoing evaluation in late-stage clinical trials for ALS.

Matrix measure-based distributed impulsive consensus on nonlinear multi-agent systems with mixed time-varying delays.

This paper concentrates on researching the global and exponential leader-following consensus issue for an array of nonlinear multi-agent systems with the system time-varying delay and the distributed time-varying delay. An innovative distributed impulsive controller is proposed to force the states of all agents to track the trajectories of leader agent. By jointly introducing the matrix measure protocol, the comparison principle for impulsive systems, and the average impulsive interval, sufficient criteria for the realization of leader-following consensus are derived. In addition, considering different functions of impulsive signal, two different convergence rates are precisely estimated by utilizing the parameter variation formula, respectively. Finally, two numerical examples are given to demonstrate the effectiveness of proposed control strategy and the rightness of theoretical analysis.

Discovery of a First-in-Class Small-Molecule Ligand for WDR91 Using DNA-Encoded Chemical Library Selection Followed by Machine Learning.

WD40 repeat-containing protein 91 (WDR91) regulates early-to-late endosome conversion and plays vital roles in endosome fusion, recycling, and transport. WDR91 was recently identified as a potential host factor for viral infection. We employed DNA-encoded chemical library (DEL) selection against the WDR domain of WDR91, followed by machine learning to predict ligands from the synthetically accessible Enamine REAL database. Screening of predicted compounds identified a WDR91 selective compound 1, with a KD of 6 ± 2 µM by surface plasmon resonance. The co-crystal structure confirmed the binding of 1 to the WDR91 side pocket, in proximity to cysteine 487, which led to the discovery of covalent analogues 18 and 19. The covalent adduct formation for 18 and 19 was confirmed by intact mass liquid chromatography-mass spectrometry. The discovery of 1, 18, and 19, accompanying structure-activity relationship, and the co-crystal structures provide valuable insights for designing potent and selective chemical tools against WDR91 to evaluate its therapeutic potential.

A sensitive LC-MS/MS assay to quantitate free payload Aur0101 from ADC PYX-201 in rat and monkey plasma.

Aim: Aur0101 is a cytotoxic and small-molecule microtubule depolymerizing agent, and is the payload conjugated to antibody-drug conjugate PYX-201. Developing and validating a sensitive bioanalytical method to quantitate Aur0101 was novel and crucial in preclinical PYX-201 studies. Materials & methods: Reference standard Aur0101 and its stable isotope labelled internal standard Aur0101-d8 were used in this LC-MS/MS method. Results: This sensitive assay was validated at a lower limit of quantitation of 15 pg/ml and successfully applied to support preclinical rat and monkey toxicology studies. Preclinical plasma toxicokinetic parameters were presented. Conclusion: A sensitive and robust LC-MS/MS assay was validated for Aur0101 in rat and monkey plasma.

Deep Learning Approach for the Discovery of Tumor-Targeting Small Organic Ligands from DNA-Encoded Chemical Libraries.

DNA-Encoded Chemical Libraries (DELs) have emerged as efficient and cost-effective ligand discovery tools, which enable the generation of protein-ligand interaction data of unprecedented size. In this article, we present an approach that combines DEL screening and instance-level deep learning modeling to identify tumor-targeting ligands against carbonic anhydrase IX (CAIX), a clinically validated marker of hypoxia and clear cell renal cell carcinoma. We present a new ligand identification and hit-to-lead strategy driven by machine learning models trained on DELs, which expand the scope of DEL-derived chemical motifs. CAIX-screening datasets obtained from three different DELs were used to train machine learning models for generating novel hits, dissimilar to elements present in the original DELs. Out of the 152 novel potential hits that were identified with our approach and screened in an in vitro enzymatic inhibition assay, 70% displayed submicromolar activities (IC50 < 1 µM). To generate lead compounds that are functionalized with anticancer payloads, analogues of top hits were prioritized for synthesis based on the predicted CAIX affinity and synthetic feasibility. Three lead candidates showed accumulation on the surface of CAIX-expressing tumor cells in cellular binding assays. The best compound displayed an in vitro KD of 5.7 nM and selectively targeted tumors in mice bearing human renal cell carcinoma lesions. Our results demonstrate the synergy between DEL and machine learning for the identification of novel hits and for the successful translation of lead candidates for in vivo targeting applications.

Embedding TiO2microspheres into the active layer as light scatterers in a perovskite photodetector to boost light harvesting.

Here, TiO2microspheres with particle sizes of 200-400 nm are embedded in p-i-n perovskite photodetectors, which are used as light scatterers. This approach was implemented to change the light transfer path in the perovskite layer, which gives the device higher photon-capture ability in a specific incident wavelength range. Compared with a pristine device, the photocurrent and responsivity of the device based on such a structure are obviously enhanced in the ranges of 560-610 nm and 730-790 nm. The photocurrent under 590 nm incident light wavelength illumination (light intensityP= 31.42µW·cm-2) increases from 1.45µA to 1.71µA, with an increase of 17.93%, and the responsivity reaches 0.305 A·W-1. In addition, the introduction of TiO2has no additional negative impact on the carrier extraction and the dark current. Also, the response time of the device did not deteriorate. Finally, the role of TiO2as light scatterers is further verified by embedding microspheres into mixed-halide perovskite devices.

Quantitation of total antibody (tAb) from antibody drug conjugate (ADC) PYX-201 in rat and monkey plasma using an enzyme-linked immunosorbent assay (ELISA) and its application in preclinical studies.

PYX-201 is an investigative ADC oncology drug composed of a monoclonal human immunoglobulin G (IgG) antibody targeting the extra domain B splice variant of fibronectin (EDB + FN) conjugated to an auristatin payload through a cleavable linker. Effective measurement of PYX-201 tAb is the key to ADC drug PYX-201 preclinical pharmacokinetics (PK) assessment. PYX-201 monoclonal antibody (mAb) was used as the reference standard, goat anti-human IgG polyclonal antibody (pAb) or rabbit anti-human Kappa light chain mAb was employed as the capture antibody, and mouse mAb or goat pAb anti-human IgG the crystallizable fragment (Fc) (horseradish peroxidase (HRP)) was utilized as the detection antibody in this ELISA. This assay was validated with a dynamic range 250 - 10,000 ng/mL and 250 - 6000 ng/mL in rat and monkey K2EDTA plasma, respectively. PYX-201 tAb bioanalytical ELISA assay was reported for the first time in any biological matrix. This is the first time for a bioanalytical method to be validated for a tAb from an ADC drug targeting EDB + FN in any biological matrix.

Filling a nick in NIK: Extending the half-life of a NIK inhibitor through structure-based drug design.

Inhibition of NF-κB inducing kinase (NIK) has been pursued as a promising therapeutic target for autoimmune disorders due to its highly regulated role in key steps of the NF-κB signaling pathway. Previously reported NIK inhibitors from our group were shown to be potent, selective, and efficacious, but had higher human dose projections than desirable for immunology indications. Herein we report the clearance-driven optimization of a NIK inhibitor guided by metabolite identification studies and structure-based drug design. This led to the identification of an azabicyclo[3.1.0]hexanone motif that attenuated in vitro and in vivo clearance while maintaining NIK potency and increasing selectivity over other kinases, resulting in a greater than ten-fold reduction in predicted human dose.

Aperiodically intermittent event-triggered pinning control on cluster synchronization of directed complex networks.

This paper is dedicated to investigating the exponential cluster synchronization in a class of nonlinearly coupled complex networks with non-identical nodes and an asymmetrical coupling matrix. A novel aperiodically intermittent pinning control (APIPC) protocol is presented, which takes full account of the cluster-tree topology structure of the networks and pins only the nodes in the current cluster that have directional links to neighboring clusters. Since it is difficult to precisely determine the intermittent control instants and rest instants of APIPC in advance, the event-triggered mechanism (ETM) is thus proposed. Based on the concept of the minimal control ratio and the segmentation analysis method, sufficient requirements for realizing the exponential cluster synchronization are derived. Moreover, the Zeno behavior of ETM is excluded by rigorous analysis. Eventually, the effectiveness and advantages of the established theorems and control strategies are demonstrated by two numerical simulations.

Aperiodically intermittent saturation consensus on multi-agent systems with discontinuous dynamics.

This paper mainly investigates the exponential consensus problem for the multi-agent systems (MASs) with nonlinear discontinuous dynamics and time-varying delay. A novel aperiodically intermittent distributed control strategy is proposed to force the state of each agent to the common trajectory, where the configuration of control width and rest width can be aperiodic. Meanwhile, in order to limit the control effects into certain reasonable ranges, an improved saturation algorithm is proposed, which effectively reduces the non-smoothness of the control signal. Sufficient conditions for the exponential consensus on the discontinuous MASs are obtained through the Filippov differential inclusion (FDI), the Lie derivative method (LDM) and the measurement selection theorem (MST). Finally, the validity and feasibility of the main theories is demonstrated by numerical simulations.

Matrix Measure-Based Event-Triggered Impulsive Quasi-Synchronization on Coupled Neural Networks.

In this article, the quasi-synchronization for a kind of coupled neural networks with time-varying delays is investigated via a novel event-triggered impulsive control approach. In view of the randomly occurring uncertainties (ROUs) in the communication channels, the global quasi-synchronization for the coupled neural networks within a given error bound is considered instead of discussing the complete synchronization. A kind of distributed event-triggered impulsive controllers is presented with considering the Bernoulli stochastic variables based on ROUs, which works at each event-triggered impulsive instant. According to the matrix measure method and the Lyapunov stability theorem, several sufficient conditions for the realization of the quasi-synchronization are successfully derived. Combining with the mathematical methodology with the formula of variation of parameters and the comparison principle for the impulsive systems with time-varying delays, the convergence rate and the synchronization error bound are precisely estimated. Meanwhile, the Zeno behaviors could be eliminated in the coupled neural network with the proposed event-triggered function. Finally, a numerical example is presented to prove the results of theoretical analysis.

Application of New Sensor Technology and Few-Shot Learning in Education Based on IoT Era.

In recent years, with the rapid development of emerging Internet of Things technology and short-range wireless communication technology, smart healthcare monitoring network technology has become a research hotspot. It provides convenience for people and enhances the development of people's own healthcare awareness. This paper aims to study how to make its application in the field of smart healthcare education more applicable through the use of related technologies in the Internet of Things era and few-shot learning. For this reason, this paper proposes to optimize and improve the new sensor technology and the algorithm of few-shot learning, and to adjust some parameters as a whole. At the same time, related experiments and analysis are designed for the improved algorithm to study and understand its performance. The experimental results in this paper show that the improved algorithm improves its application effect by 36.9% and is relatively more applicable than the unimproved algorithm.

Fixed-Time Synchronization of Complex Dynamical Networks: A Novel and Economical Mechanism.

Fixed-time synchronization of complex networks is investigated in this article. First, a completely novel lemma is introduced to prove the fixed-time stability of the equilibrium of a general ordinary differential system, which is less conservative and has a simpler form than those in the existing literature. Then, sufficient conditions are presented to realize synchronization of a complex network (with a target system) within a settling time via three different kinds of simple controllers. In general, controllers designed to achieve fixed-time stability consist of three terms and are discontinuous. However, in our mechanisms, the controllers only contain two terms or even one term and are continuous. Thus, our controllers are simpler and of more practical applicability. Finally, three examples are provided to illustrate the correctness and effectiveness of our results.

Influence of aging on chronic unpredictable mild stress-induced depression-like behavior in male C57BL/6J mice.

Depression is a common psychiatric disorder that can occur throughout an individual's lifespan. Chronic unpredictable mild stress (CUMS) protocol is currently the most commonly used to develop an animal model of depression. Due to the variable duration and procedure of CUMS, it is difficult to reproduce and explore the mechanism of CUMS-induced depression effectively. In the present study, the CUMS-induced behavioral phenotypes were assessed in male C57BL/6J mice at the age of 9-18 weeks. The mice stressed for 3-8 weeks exhibited lower body weight as well as longer immobility time of forced swim and tail suspension test compared to control mice. Moreover, lessening and impairment of hippocampal neurons was found in stressed mice at the age of 18 weeks, which was correlated with increased relative mRNA expression levels of inflammatory cytokines BDNF, Htr1a, and IL-6 in the hippocampus. Nevertheless, no difference between stressed and control mice was observed neither in the sucrose preference nor in the open field test (except for vertical activity in OFT) at the age of 18 weeks. These findings reveal that 3-8 weeks of chronic stress could induce depression-like alterations in male C57BL/6J mice and the behavioral adaptation of aged mice might fail to the availability of the depression model.

Fixed-Time Synchronization of Coupled Neural Networks With Discontinuous Activation and Mismatched Parameters.

This article is concerned with fixed-time synchronization of the nonlinearly coupled neural networks with discontinuous activation and mismatched parameters. First, a novel lemma is proposed to study fixed-time stability, which is less conservative than those in most existing results. Then, based on the new lemma, a discontinuous neural network with mismatched parameters will synchronize to the target state within a settling time via two kinds of unified and simple controllers. The settling time is theoretically estimated, which is independent of the initial values of the considered network. In particular, the estimated settling time is closer to the real synchronization time than those given in the existing literature. Finally, two numerical simulations are presented to illustrate the effectiveness and correctness of our results.

Inhibition of Discharge Side Reactions by Promoting Solution-Mediated Oxygen Reduction Reaction with Stable Quinone in Li-O2 Batteries.

Aprotic lithium-oxygen (Li-O2) batteries with an ultrahigh theoretical energy density have great potential in rechargeable power supply, while their application still faces several challenges, especially poor cycle stability. To solve the problems, one of the effective strategies is to inhibit the generation of the LiO2 intermediate produced via a surface-mediated oxygen reduction reaction (ORR) pathway, which is an important species inducing byproduct generation and low cell cyclic stability. Herein, a series of quinones and solid materials serve as the solution-mediated and surface-mediated ORR catalysts, and it was found that the generation of LiO2 and byproducts from solid catalysts was inhibited by quinones. Among the studied quinones, benzo[1,2-b:4,5-b']dithiophene-4,8-dione, a quinone molecule with the advantage of a highly symmetrical planar and conjugated structure and without α-H, exhibits high redox potential, diffusion coefficient, and electrochemical stability, and consequently the best ORR activities and the capability to inhibit byproduct generation. It indicated that the increase of the solution-mediated ORR pathway plays an important role in restraining the discharging side reaction, substantially improving cell cycle stability and capacity. This study provides the theoretical and experimental basis for better understanding the ORR process of Li-O2 batteries.

Depression-/Anxiety-Like Behavior Alterations in Adult Slit2 Transgenic Mice.

Background: Slit2 is a member of the Slit family of secreted glycoproteins that plays highly conserved roles in neuronal axon guidance and cellular migration. Our previous experimental results showed Alzheimer's disease-like alterations and increased permeability of the blood-brain barrier in Slit2-overexpressing transgenic (Slit2-Tg) mice aged 8-9 months. Nevertheless, relatively little is known about behavioral alterations in adult Slit2-Tg mice (2-6 months of age). To observe the age-related behavioral effects of Slit2 overexpression in adult mice, we performed a battery of behavioral tests with adult Slit2-Tg mice at 2-6 months of age. Results: The body weight of Slit2-Tg mice was lower than that of the wild-type mice from 15 weeks of age. Compared with the control mice, depression-like behaviors were found in Slit2-Tg mice from 15 to 21 weeks of age in the sucrose preference test, although Slit2-Tg mice were hyperactive in the tail suspension test. The anxiety-like behaviors were found in Slit2-Tg mice in the open field test, as well as increased locomotor activity. The anxiety-like behaviors were also found in adult Slit2-Tg mice in the elevated plus maze. Compared to wild-type mice at 23 weeks old, impairment of the hippocampal neurons were found in Slit2-Tg mice at the same age in hematoxylin-eosin staining (H&E), including some eccentric dispersion and expansion of neuronal bodies. In addition, the messenger RNA (mRNA) expression of TNF-α was elevated in the hippocampus of adult Slit2-Tg mice. Conclusions: Slit2 overexpression causes depression-/anxiety-like behaviors in adult mice that may be related to an increase in inflammatory factors and damage to hippocampal neurons.

An efficient polymer coating for highly acid-stable zeolitic imidazolate frameworks based composite sponges.

Zeolitic imidazolate frameworks (ZIFs) possess tremendous potential in various adsorption and catalysis areas for their particular structures. However, the dispersibility and acid stability of ZIFs are important issues hindering their applications. To address these challenges, a transparent polydimethysiloxane (PDMS) coating was constructed to heterogeneously anchor the Cu doped ZIF-67 (Cu/ZIF-67) nanoparticles on melamine sponge surface, achieving a PDMS-coated ZIF three-dimensional composite sponge. Thus PDMS coating could also effectively protect ZIFs from acid damage to prolong the service life of photocatalyticity. It was demonstrated that the composite sponges were able to repeatedly (over 40 cycles) degrade Sudan I dyes with remarkable photocatalytic efficiency (>97%). More importantly, the ion impenetrability of PDMS coating made the ZIFs based composite a longer term catalytic life than unprotected Cu/ZIF-67 under acid condition. Incidentally, due to the introduction of rough ZIFs and hydrophobic PDMS coating, the obtained sponge also exhibits super-hydrophobicity (158.5°), great compressibility and excellent oil/acid water separation performance. We expect that such a polymer coating strategy could act as a novel inspiration for extending the applications and life span of ZIF-based composites.

Structure Based Design of Potent Selective Inhibitors of Protein Kinase D1 (PKD1).

We previously disclosed a series of type I 1/2 inhibitors of NF-κB inducing kinase (NIK). Inhibition of NIK by these compounds was found to be strongly dependent on the inclusion and absolute stereochemistry of a propargyl tertiary alcohol as it forms critical hydrogen bonds (H-bonds) with NIK. We report that inhibition of protein kinase D1 (PKD1) by this class of compounds is not dependent on H-bond interactions of this tertiary alcohol. This feature was leveraged in the design of highly selective inhibitors of PKD1 that no longer inhibit NIK. A structure-based hypothesis based on the position and flexibility of the α-C-helix of PKD1 vs NIK is presented.