Dynamic State Changes Research Articles

Dormancy-break and germination of buried milk thistle (Silybum marianum (L.) Gaertn.) seeds

Milk thistle (Silybum marianum (L.) Gaertn.) is a medicinal/oil crop plant that produces dormant seeds at maturity. Seeds are released before harvest into the soil seed bank and hence can cause weed control problems in agricultural ecosystems. So far, information on dynamic changes of dormancy state, viability and persistence of milk thistle seeds in the soil seed bank is lacking. Accordingly, in this study the effects of ex-situ (dry in laboratory) and in-situ (buried in soil) storage conditions on dormancy-break and germination were investigated. Freshly harvested (FH) seeds were tested for germination at constant temperature ranging from 5 to 35 °C under both light/dark and dark conditions with or without GA3. Also, FH seeds were buried in June 2019 at depths of 5, 10, and 20 cm in a field and exhumed after 2–12 months of burial and assessed for germination. FH seeds did not germinate at any test conditions, but GA3 and after-ripening in dry storage increased germination, indicating that seeds have non-deep physiological dormancy (PD). With increased dry after-ripening, the ceiling temperature for germination (30 °C) did not change but the base temperature for germination decreased to 5 °C, i.e, seeds exhibited type 2 non-deep PD. Increased seed burial depth increased the length of the in situ after ripening period necessary for complete dormancy alleviation. Furthermore, persistence of seeds in the soil was affected by burial depth. Seeds buried at 5 cm had a transient seed bank and those at 10 and 20 cm a persistent seed bank.

Dynamic changes in seizure state and anxiety-like behaviors during pentylenetetrazole kindling in rats

IntroductionExcessive anxiety is a mental disorder, and its treatment involves the use of benzodiazepines, a class of drugs that enhance the effects of the neurotransmitter gamma-aminobutyric acid (GABA) at the GABAA receptor. Anxiety disorders are frequent comorbidities in patients with epilepsy, and it has been speculated that anxiety disorders and epileptic seizures share common neurobiological mechanisms. However, conflicting results regarding anxiolytic and anxiogenic effects have been reported in animal models of epilepsy induced by pentylenetetrazole (PTZ) injections, and the causes of this discrepancy are unknown. We hypothesized that anxiety-like behaviors would change dynamically according to the changes in epilepsy susceptibility that occur during the PTZ kindling process. Therefore, we attempted to change anxiety-like behaviors bidirectionally depending on the number of PTZ injections. MethodsAdult male rats were injected with PTZ 20 times every other day, and stages of seizure onset were classified according to the Racine staging system. Anxiety-like behaviors were measured after 10 and 20 injections. The control group was injected with an equal volume of saline solution. Anxiety-like behaviors were investigated using the open-field, light/dark transition, elevated plus maze, and social interaction tests. ResultsBimodal changes in seizure stage were observed in response to PTZ kindling. The increase in the seizure stage was transiently suppressed after 10 injections, and this decrease in epileptic sensitivity disappeared after 20 injections. However, none of the rats reached a fully kindled state after 20 PTZ injections. After 10 PTZ injections, anxiety-like behaviors decreased compared with those of the control group in the open field, light/dark transition, and elevated plus-maze tests. The anxiolytic effects correlated with the seizure stage in individual rats. After 20 PTZ injections, anxiety-like behaviors returned to control levels. ConclusionPTZ kindling induced bimodal changes in the seizure stage. Anxiety-like behaviors decreased with transient decrease in epileptic sensitivity and returned to control levels with the disappearance of these states. These findings suggest a common neurobiological mechanism underlying anxiety disorders and epileptic seizures. In addition, the discrepancy in the previous studies, in which anxiety levels increase or decrease in PTZ-kindled animals, may be due to examination at different phases of the kindling process.

Associations of ambient air pollution with incidence and dynamic progression of atrial fibrillation

The influence of air pollution on dynamic changes in clinical state from healthy to atrial fibrillation (AF), further AF-related complications and ultimately, death are unclear. We aimed to investigate the relationships between air pollution and the occurrence and progression trajectories of AF. We retrieved 442,150 participants free of heart failure (HF), myocardial infarction (MI), stroke and dementia at baseline from UK Biobank. Exposures to air pollution for each transition stage were estimated at the geocoded residential address of each participant using the bilinear interpolation approach. The outcomes were incident AF, complications, and death. Multi-stage models were used to evaluate the associations between air pollution and dynamic progression of AF. Over a 12.6-year median follow-up, a total of 21,670 incident AF patients were identified, of whom, 4103 developed complications and 1331 died. PM2.5, PM10, NOx and NO2 were differentially positively associated, while O3 was negatively associated with risks of progression trajectories of AF. PM2.5 exposure was significantly associated with an increased risk of progression. The associations of PM2.5, PM10, NOx, and NO2 on incident AF were generally more pronounced compared to other transitions. The cumulative transition probabilities were generally higher in individuals with higher exposure levels of PM2.5, PM10, NOx, and NO2 and lower exposure to O3. Air pollution could potentially have a role in increasing the risk of both the occurrence and progression of AF, emphasizing the significance of air pollution interventions in both the primary prevention of AF and the management of AF-related outcomes.

Unveiling Key Interface Characteristics of Ni/Yttria-Stabilized Zirconia Solid Oxide Cell Electrodes in H2O Electroreduction Using Operando X-ray Photoelectron Spectroscopy.

Nickel/yttria-stabilized zirconia (YSZ) composites are the most commonly used fuel electrodes for solid oxide cells. While microstructural changes of Ni/YSZ during operational conditions have been thoroughly investigated, there is limited knowledge regarding Ni/YSZ surface chemistry under working conditions. In this study, we examine the interaction between Ni/YSZ electrodes and water vapor under open circuit and polarization conditions, utilizing near ambient pressure soft and hard X-ray photoelectron spectroscopies. Miniature cells with conventional porous Ni/YSZ composite cermet cathodes were modified to facilitate the direct spectroscopic observation of the functional electrode's areas close to the interface with the YSZ electrolyte. The results highlight dynamic changes in the oxidation state and composition of Ni/YSZ under H2 and H2O atmospheres. We also quantify the accumulation of impurities on the electrode surface. Through adjustments in the pretreatment of the cell, the correlation between the nickel surface oxidation state and the cell's electrochemical performance during H2O electroreduction is established. It is unequivocally shown that nickel surface oxidation in H2O electrolysis favors NiO over Ni(OH)x, providing critical insights into the mechanism of Ni-phase redistribution within the electrode during long-term operation. Depth-dependent photoemission measurements, combined with theoretical quantitative simulations, reveal that NiO and Ni phases are uniformly mixed on the surface during H2O electrolysis. This differs from the conventional expectation of a NiO-shell/Ni-core configuration in gas phase oxidation. These findings provide crucial insights into the surface chemistry of Ni/YSZ electrodes under conditions relevant to H2O electrolysis, elucidating their impact on the electrochemical performance of the cell.

Topological state-space estimation of functional human brain networks.

We introduce an innovative, data-driven topological data analysis (TDA) technique for estimating the state spaces of dynamically changing functional human brain networks at rest. Our method utilizes the Wasserstein distance to measure topological differences, enabling the clustering of brain networks into distinct topological states. This technique outperforms the commonly used k-means clustering in identifying brain network state spaces by effectively incorporating the temporal dynamics of the data without the need for explicit model specification. We further investigate the genetic underpinnings of these topological features using a twin study design, examining the heritability of such state changes. Our findings suggest that the topology of brain networks, particularly in their dynamic state changes, may hold significant hidden genetic information.

Longitudinal molecular profiling elucidates immunometabolism dynamics in breast cancer

Although metabolic reprogramming within tumor cells and tumor microenvironment (TME) is well described in breast cancer, little is known about how the interplay of immune state and cancer metabolism evolves during treatment. Here, we characterize the immunometabolic profiles of tumor tissue samples longitudinally collected from individuals with breast cancer before, during and after neoadjuvant chemotherapy (NAC) using proteomics, genomics and histopathology. We show that the pre-, on-treatment and dynamic changes of the immune state, tumor metabolic proteins and tumor cell gene expression profiling-based metabolic phenotype are associated with treatment response. Single-cell/nucleus RNA sequencing revealed distinct tumor and immune cell states in metabolism between cold and hot tumors. Potential drivers of NAC based on above analyses were validated in vitro. In summary, the study shows that the interaction of tumor-intrinsic metabolic states and TME is associated with treatment outcome, supporting the concept of targeting tumor metabolism for immunoregulation.

Electrocatalytic Nitrate and Nitrite Reduction toward Ammonia Using Cu2O Nanocubes: Active Species and Reaction Mechanisms.

The electrochemical reduction of nitrate (NO3-) and nitrite (NO2-) enables sustainable, carbon-neutral, and decentralized routes to produce ammonia (NH3). Copper-based materials are promising electrocatalysts for NOx- conversion to NH3. However, the underlying reaction mechanisms and the role of different Cu species during the catalytic process are still poorly understood. Herein, by combining quasi in situ X-ray photoelectron spectroscopy (XPS) and operando X-ray absorption spectroscopy (XAS), we unveiled that Cu is mostly in metallic form during the highly selective reduction of NO3-/NO2- to NH3. On the contrary, Cu(I) species are predominant in a potential region where the two-electron reduction of NO3- to NO2- is the major reaction. Electrokinetic analysis and in situ Raman spectroscopy was also used to propose possible steps and intermediates leading to NO2- and NH3, respectively. This work establishes a correlation between the catalytic performance and the dynamic changes of the chemical state of Cu, and provides crucial mechanistic insights into the pathways for NO3-/NO2- electrocatalytic reduction.

Dynamic Copper Site Redispersion through Atom Trapping in Zeolite Defects.

Single-site copper-based catalysts have shown remarkable activity and selectivity for a variety of reactions. However, deactivation by sintering in high-temperature reducing environments remains a challenge and often limits their use due to irreversible structural changes to the catalyst. Here, we report zeolite-based copper catalysts in which copper oxide agglomerates formed after reaction can be repeatedly redispersed back to single sites using an oxidative treatment in air at 550 °C. Under different environments, single-site copper in Cu-Zn-Y/deAlBeta undergoes dynamic changes in structure and oxidation state that can be tuned to promote the formation of key active sites while minimizing deactivation through Cu sintering. For example, single-site Cu2+ reduces to Cu1+ after catalyst pretreatment (270 °C, 101 kPa H2) and further to Cu0 nanoparticles under reaction conditions (270-350 °C, 7 kPa EtOH, 94 kPa H2) or accelerated aging (400-450 °C, 101 kPa H2). After regeneration at 550 °C in air, agglomerated CuO was dispersed back to single sites in the presence and absence of Zn and Y, which was verified by imaging, in situ spectroscopy, and catalytic rate measurements. Ab initio molecular dynamics simulations show that solvation of CuO monomers by water facilitates their transport through the zeolite pore, and condensation of the CuO monomer with a fully protonated silanol nest entraps copper and reforms the single-site structure. The capability of silanol nests to trap and stabilize copper single sites under oxidizing conditions could extend the use of single-site copper catalysts to a wider variety of reactions and allows for a simple regeneration strategy for copper single-site catalysts.

Elucidating dynamic conductive state changes in amorphous lithium lanthanum titanate for resistive switching devices

Exploration of novel resistive switching materials attracts attention to achieve neuromorphic computing that can surpass the limit of the current Von-Neumann computing for the time of Internet of Things (IoT). Battery materials priorly used to serve as an electrode or electrolyte have demonstrated metal-insulator transitions upon an electrical biasing due to resulting compositional change, which is desirable property for future resistive switching devices. For example, it has been suggested that solid-state electrolyte amorphous lithium lanthanum titanate (a-LLTO) changes in electronic conductivity depending on oxygen content. In this work, switching behavior of a-LLTO was investigated at both bulk- and nano-scale by employing a range of voltage sweep techniques, ultimately establishing a stable and optimal operating condition within the voltage window of − 3.5 V to 3.5 V. This voltage range effectively balances the desirable trait of a substantial resistance change by three orders of magnitude with the imperative avoidance of LLTO decomposition. Experiment and computation with different LLTO composition shows that LLTO has two distinct conductivity states due to Ti reduction. The distribution of these two states is discussed using simplified binary model, implying the conductive filament growth during low resistance state. Consequently, our study deepens understanding of LLTO electronic properties and encourages the interdisciplinary application of battery materials for resistive switching devices.

Extract latent features of single-particle trajectories with historical experience learning

Single-particle tracking has enabled real-time, in situ quantitative studies of complex systems. However, inferring dynamic state changes from noisy and undersampling trajectories encounters challenges. Here, we introduce a data-driven method for extracting features of subtrajectories with historical experience learning (Deep-SEES), where a single-particle tracking analysis pipeline based on a self-supervised architecture automatically searches for the latent space, allowing effective segmentation of the underlying states from noisy trajectories without prior knowledge on the particle dynamics. We validated our method on a variety of noisy simulated and experimental data. Our results showed that the method can faithfully capture both stable states and their dynamic switch. In highly random systems, our method outperformed commonly used unsupervised methods in inferring motion states, which is important for understanding nanoparticles interacting with living cell membranes, active enzymes, and liquid-liquid phase separation. Self-generating latent features of trajectories could potentially improve the understanding, estimation, and prediction of many complex systems.

Mapping the epigenomic landscape of human monocytes following innate immune activation reveals context-specific mechanisms driving endotoxin tolerance

BackgroundMonocytes are key mediators of innate immunity to infection, undergoing profound and dynamic changes in epigenetic state and immune function which are broadly protective but may be dysregulated in disease. Here, we aimed to advance understanding of epigenetic regulation following innate immune activation, acutely and in endotoxin tolerant states.MethodsWe exposed human primary monocytes from healthy donors (n = 6) to interferon-γ or differing combinations of endotoxin (lipopolysaccharide), including acute response (2 h) and two models of endotoxin tolerance: repeated stimulations (6 + 6 h) and prolonged exposure to endotoxin (24 h). Another subset of monocytes was left untreated (naïve). We identified context-specific regulatory elements based on epigenetic signatures for chromatin accessibility (ATAC-seq) and regulatory non-coding RNAs from total RNA sequencing.ResultsWe present an atlas of differential gene expression for endotoxin and interferon response, identifying widespread context specific changes. Across assayed states, only 24–29% of genes showing differential exon usage are also differential at the gene level. Overall, 19.9% (6,884 of 34,616) of repeatedly observed ATAC peaks were differential in at least one condition, the majority upregulated on stimulation and located in distal regions (64.1% vs 45.9% of non-differential peaks) within which sequences were less conserved than non-differential peaks. We identified enhancer-derived RNA signatures specific to different monocyte states that correlated with chromatin accessibility changes. The endotoxin tolerance models showed distinct chromatin accessibility and transcriptomic signatures, with integrated analysis identifying genes and pathways involved in the inflammatory response, detoxification, metabolism and wound healing. We leveraged eQTL mapping for the same monocyte activation states to link potential enhancers with specific genes, identifying 1,946 unique differential ATAC peaks with 1,340 expression associated genes. We further use this to inform understanding of reported GWAS, for example involving FCHO1 and coronary artery disease.ConclusionThis study reports context-specific regulatory elements based on transcriptomic profiling and epigenetic signatures for enhancer-derived RNAs and chromatin accessibility in immune tolerant monocyte states, and demonstrates the informativeness of linking such elements and eQTL to inform future mechanistic studies aimed at defining therapeutic targets of immunosuppression and diseases.

Deep reinforcement learning-based edge computing offloading algorithm for software-defined IoT

Edge computing offloading can effectively solve the problem of insufficient computing resources for terminal devices and improve the performance and efficiency of the system. When network states and tasks change rapidly, data-driven intelligent algorithms have difficulty obtaining comprehensive statistics for accurate prediction, resulting in degraded performance of computational offloading and difficulty in adaptive adjustment. It is a current challenge to improve the environment-aware, intelligent optimization so that the computational offloading algorithm can adapt to the dynamic changes in network state and task demands, thus achieving global multi-objective optimization. This paper presents optimized edge computing offloading algorithm for software-defined IoT. First, to provide global state for making decisions, a software defined edge computing (SDEC) architecture is proposed. The edge layer is integrated into the control layer of software-defined IoT, and multiple controllers share the global network state information via east–west message exchange. Moreover, an edge computing offloading algorithm in software-defined IoT (ECO-SDIoT) based on deep reinforcement learning is proposed. It enables the controllers to offload the computing task to the most appropriate edge server according to the global states, task requirements, and reward. Finally, the performance metrics for edge computing offloading were evaluated in terms of unit task processing latency, load balancing of edge servers, task processing energy consumption, and task completion rate, respectively. Simulation results show that ECO-SDIoT can effectively reduce task completion time and energy consumption compared with other strategies.

Elucidating Dynamic Conductive State Changes in Amorphous LLTO for Resistive Switching Devices

Resistive random-access memory (ReRAM) is one of the promising memory devices to achieve neuromorphic computing that is a novel system to mimic human brain. ReRAM has a simple metal-insulator-metal structure and has advantages such as low power consumption and low manufacturing cost. Recently, our group has developed amorphous lithium lanthanum titanate (Li3xLa2/3-xTiO3, LLTO) thin film as a solid-state electrolyte for all solid-state thin-film batteries by using pulsed laser deposition, which has high ionic conductivity (≈10-3 S/cm) [1]. Beyond its application to energy storage devices, we found that the amorphous LLTO (a-LLTO) demonstrates a resistive switching response under the stimuli of an external electric field, where LLTO becomes high resistance state (HRS) at pristine state and switches to low resistance state (LRS) by external electric field. However, despite its unique feature, the resistive switching mechanism of a-LLTO is poorly understood.Thus, in this work, we demonstrate successful observation of LLTO resistive switching behavior and optimize voltage range to achieve both resistive switching and cycling stability. Furthermore, electronic conductivity changes that provoke the LLTO switching are investigated through the comparison of thin films with different oxygen composition, under an hypothesis that oxygen vacancy plays a role in changing local LLTO conductivity. First-principles analysis also supports such conductivity change mechanism due to the local composition changes under electrical bias. Based on these experimental and computational results, the switching mechanisms are discussed with a simplified LLTO model. At last, in-situ testing of LLTO nano device under focused ion beam (FIB) setup, which aims for scanning transmission electron microscopy (STEM)-electron energy loss spectroscopy (EELS) to fully elucidate the switching mechanisms of LLTO at nanoscale.Reference[1] Lee, J. Z.; Wang, Z.; Xin, H. L.; Wynn, T. A.; Meng, Y. S. Amorphous Lithium Lanthanum Titanate for Solid-State Microbatteries. J. Electrochem. Soc. 2016, 164 (1), A6268. Figure 1

EventKGE: Event knowledge graph embedding with event causal transfer

Traditional knowledge graph embedding (KGE) aims to map entities and relations into continuous space vectors to provide high-quality data feature representation for downstream tasks. However, relations in most KGs often only reflect connections between static entities, but cannot represent dynamic activities and state changes of related entities, which makes the KGE models unable to effectively learn rich and comprehensive entity representation. In this paper, we verify the importance of embedding event knowledge in KG representation learning, and propose a novel event KGE model based on event causal transfer (EventKGE), which can effectively maintain the semantic information of events, entities, and relations in the event KG. First, we define a six tuple-based event representation model consisting of event trigger words, event arguments and event description text, which can effectively express events in a structured form. Second, for a given event KG, the event nodes and entity nodes in the KG are integrated through the constructed heterogeneous graph. Meanwhile, in the heterogeneous graph, the event nodes and entity nodes are connected through the event arguments type, and the event nodes are connected through the causal relationship. Finally, an information transfer method based on an attention network is designed, which is used for the relations between events, events and entities, and entities and entities, to integrate event information into KGE. Comprehensive experiments on the ACE2005 corpus and CEC2.0 dataset verify that the event KGE model we designed is efficient and stable in multiple downstream tasks.

Redox state and metabolic responses to severe heat stress in lenok Brachymystax lenok (Salmonidae).

In order to provide new insights into the physiological responses of lenok (Brachymystax lenok: Salmonidae) to acute and severe heat stress (25°C, 48h), dynamic changes in redox state and metabolic responses are studied combined biochemical index and non-targeted metabolome. Nicotinamide adenine dinucleotide (NAD+) consumption causes significant increases in ratio of reduced NADH to NAD+ and ratio of reduced nicotinamide adenine dinucleotide phosphate (NADPH) to NADP+, which induced the redox imbalance in heat stressed lenok. Lowered reduced glutathione/oxidized glutathione (GSH/GSSG) ratios suggested that more oxidized conditions occurred in heat-stressed lenok, leading to membrane lipid oxidation. The first few hours of heat stress promoted the activity of enzymes involved in anaerobic glycolysis (hexokinase, pyruvate kinase, lactic dehydrogenase) and glutamicpyruvic transaminase and glutamic oxaloacetic transaminase, which might lead to consumption of many carbohydrates and amino acid catabolism. These enzyme activities decreased with time in a possible compensatory strategy to manage anabolic and catabolic metabolism, maintaining the redox homeostasis. After 48 h of recovery, NAD+, carbohydrate levels and enzyme activities had returned to control levels, whereas many amino acids were consumed for repair and new synthesis. GSH remained at levels lower than controls, and the more oxidized conditions had not recovered, aggravating oxidative damage. Glutamic acid, glutamine, lysine and arginine may play important roles in survival of heat-stressed lenok.

Integrating Intensive Longitudinal Data (ILD) to Inform the Development of Dynamic Theories of Behavior Change and Intervention Design: a Case Study of Scientific and Practical Considerations.

The increasing sophistication of mobile and sensing technology has enabled the collection of intensive longitudinal data (ILD) concerning dynamic changes in an individual's state and context. ILD can be used to develop dynamic theories of behavior change which, in turn, can be used to provide a conceptual framework for the development of just-in-time adaptive interventions (JITAIs) that leverage advances in mobile and sensing technology to determine when and how to intervene. As such, JITAIs hold tremendous potential in addressing major public health concerns such as cigarette smoking, which can recur and arise unexpectedly. In tandem, a growing number of studies have utilized multiple methods to collect data on a particular dynamic construct of interest from the same individual. This approach holds promise in providing investigators with a significantly more detailed view of how a behavior change processes unfold within the same individual than ever before. However, nuanced challenges relating to coarse data, noisy data, and incoherence among data sources are introduced. In this manuscript, we use a mobile health (mHealth) study on smokers motivated to quit (Break Free; R01MD010362) to illustrate these challenges. Practical approaches to integrate multiple data sources are discussed within the greater scientific context of developing dynamic theories of behavior change and JITAIs.

A Sub-flow Adaptive Multipath Routing Algorithm for Data Centre Network

In response to the fast and intensive traffic changes and the presence of redundant links in the network topology in data centre networks, multipath routing has become the dominant approach. Existing multipath routing algorithms are still inadequate in terms of adapting to dynamic changes in the network state quickly and the cooperation of path selection and sub-flow assignment. Therefore, this paper proposes a sub-flow adaptive multipath routing algorithm for data centre networks (SAMP). The deep deterministic policy gradient (DDPG) algorithm is introduced. DDPG combines deep learning (DL) and reinforcement learning (RL) to implement different network state changes quickly, especially in the process of topology, to achieve dynamic migration of the optimised decisions that have been learned. An adaptive multipath routing model for subflows is established to accomplish collaborative scheduling of path selection and subflow assignment based on the real-time state of the network. The experimental results show that the algorithm can be well adapted to the data centre network, and when compared with several traditional methods, it reduces the latency by an average of 31.3%, improves the task transmission success rate by an average of 14.9%, and increases the throughput by 37.1%.

Hybrid Momentum Compensation Control by Using Arms for Bipedal Dynamic Walking

Biped robots swing their legs alternately to achieve highly dynamic walking, which is the basic ability required for them to perform tasks. However, swinging of the swinging leg in the air will disturb the interaction between the supporting leg and the ground and affect the upper body's balance during dynamic walking. To allow the robot to use its own intrinsic motion characteristics to maintain stable movement like a human when its lower limbs are affected by unknown disturbances during dynamic walking, the ability to use its arms to resist disturbances is essential. This article presents a hybrid momentum compensation control method for torque-controlled biped robots to adapt to unknown disturbances during dynamic walking. First, a hybrid angular momentum and linear momentum regulator is designed to compensate for the disturbance caused by the swinging leg. Second, based on real-time dynamic state changes of the legs, a mixed-momentum quadratic programming controller is designed to realize stable dynamic walking. The proposed method allows the force-controlled robot to maintain its balance while walking down an unknown platform, and it maintains good straightness in the forward direction of dynamic motion. The proposed method's effectiveness is verified experimentally on the BHR-B2 force-controlled biped robot platform.

Spatial Chemoproteomics for Mapping the Active Proteome.

Functional regulation of cell signaling through dynamic changes in protein activity state as well as spatial organization represent two dynamic, complex, and conserved phenomena in biology. Seemingly separate areas of -omics method development have focused on building tools that can detect and quantify protein activity states, as well as map sub-cellular and intercellular protein organization. Integration of these efforts, through the development of chemical tools and platforms that enable detection and quantification of protein functional states with spatial resolution provide opportunities to better understand heterogeneity in the proteome within cell organelles, multi-cellular tissues, and whole organisms. This review provides an overview of and considerations for major classes of chemical proteomic probes and technologies that enable protein activity mapping from sub-cellular compartments to live animals.

Discovery and Optimization of N-Acyl-6-sulfonamide-tetrahydroquinoline Derivatives as Novel Non-Steroidal Selective Glucocorticoid Receptor Modulators.

Selective glucocorticoid receptor modulators (SGRMs), which can dissociate the transactivation from the transrepression of the glucocorticoid receptor (GR), are regarded as very promising therapeutics for inflammatory and autoimmune diseases. We previously discovered a SGRM HP-19 based on the passive antagonistic conformation of GR and bioassays. In this study, we further analyzed the dynamic changes of the passive antagonistic state upon the binding of HP-19 and designed and synthesized 62 N-acyl-6-sulfonamide-tetrahydroquinoline derivatives by structural optimization of HP-19. Therein, compound B53 exhibits the best transrepression activity (IC50 NF-κB = 0.009 ± 0.001 μM) comparable with dexamethasone (IC50 NF-κB = 0.005 ± 0.001 μM) and no transactivation activity. B53 can efficiently reduce the expression of inflammatory factors IL-6, IL-1β, TNF-α, and so on and makes a milder adverse effect and is highly specific to GR. Furthermore, B53 is able to significantly relieve dermatitis on a mouse model via oral drug intervention.