Classical Signature Research Articles

Novel genetic features of human and mouse Purkinje cell differentiation defined by comparative transcriptomics

Comparative transcriptomics between differentiating human pluripotent stem cells (hPSCs) and developing mouse neurons offers a powerful approach to compare genetic and epigenetic pathways in human and mouse neurons. To analyze human Purkinje cell (PC) differentiation, we optimized a protocol to generate human pluripotent stem cell-derived Purkinje cells (hPSC-PCs) that formed synapses when cultured with mouse cerebellar glia and granule cells and fired large calcium currents, measured with the genetically encoded calcium indicator jRGECO1a. To directly compare global gene expression of hPSC-PCs with developing mouse PCs, we used translating ribosomal affinity purification (TRAP). As a first step, we used Tg(Pcp2-L10a-Egfp) TRAP mice to profile actively transcribed genes in developing postnatal mouse PCs and used metagene projection to identify the most salient patterns of PC gene expression over time. We then created a transgenic Pcp2-L10a-Egfp TRAP hPSC line to profile gene expression in differentiating hPSC-PCs, finding that the key gene expression pathways of differentiated hPSC-PCs most closely matched those of late juvenile mouse PCs (P21). Comparative bioinformatics identified classical PC gene signatures as well as novel mitochondrial and autophagy gene pathways during the differentiation of both mouse and human PCs. In addition, we identified genes expressed in hPSC-PCs but not mouse PCs and confirmed protein expression of a novel human PC gene, CD40LG, expressed in both hPSC-PCs and native human cerebellar tissue. This study therefore provides a direct comparison of hPSC-PC and mouse PC gene expression and a robust method for generating differentiated hPSC-PCs with human-specific gene expression for modeling developmental and degenerative cerebellar disorders.

Observation and Modeling of High-temperature Solar Active Region Emission during the High-resolution Coronal Imager Flight of 2018 May 29

Abstract Excellent coordinated observations of NOAA active region 12712 were obtained during the flight of the High-resolution Coronal Imager (Hi-C) sounding rocket on 2018 May 29. This region displayed a typical active region core structure with relatively short, high-temperature loops crossing the polarity inversion line and bright “moss” located at the footpoints of these loops. The differential emission measure (DEM) in the active region core is very sharply peaked at about 4 MK. Further, there is little evidence for impulsive heating events in the moss, even at the high spatial resolution and cadence of Hi-C. This suggests that active region core heating is occurring at a high frequency and keeping the loops close to equilibrium. To create a time-dependent simulation of the active region core, we combine nonlinear force-free extrapolations of the measured magnetic field with a heating rate that is dependent on the field strength and loop length and has a Poisson waiting time distribution. We use the approximate solutions to the hydrodynamic loop equations to simulate the full ensemble of active region core loops for a range of heating parameters. In all cases, we find that high-frequency heating provides the best match to the observed DEM. For selected field lines, we solve the full hydrodynamic loop equations, including radiative transfer in the chromosphere, to simulate transition region and chromospheric emission. We find that for heating scenarios consistent with the DEM, classical signatures of energy release, such as transition region brightenings and chromospheric evaporation, are weak, suggesting that they would be difficult to detect.

Unseen Land Cover Classification from High-Resolution Orthophotos Using Integration of Zero-Shot Learning and Convolutional Neural Networks

Zero-shot learning (ZSL) is an approach to classify objects unseen during the training phase and shown to be useful for real-world applications, especially when there is a lack of sufficient training data. Only a limited amount of works has been carried out on ZSL, especially in the field of remote sensing. This research investigates the use of a convolutional neural network (CNN) as a feature extraction and classification method for land cover mapping using high-resolution orthophotos. In the feature extraction phase, we used a CNN model with a single convolutional layer to extract discriminative features. In the second phase, we used class attributes learned from the Word2Vec model (pre-trained by Google News) to train a second CNN model that performed class signature prediction by using both the features extracted by the first CNN and class attributes during training and only the features during prediction. We trained and tested our models on datasets collected over two subareas in the Cameron Highlands (training dataset, first test dataset) and Ipoh (second test dataset) in Malaysia. Several experiments have been conducted on the feature extraction and classification models regarding the main parameters, such as the network’s layers and depth, number of filters, and the impact of Gaussian noise. As a result, the best models were selected using various accuracy metrics such as top-k categorical accuracy for k = [1,2,3], Recall, Precision, and F1-score. The best model for feature extraction achieved 0.953 F1-score, 0.941 precision, 0.882 recall for the training dataset and 0.904 F1-score, 0.869 precision, 0.949 recall for the first test dataset, and 0.898 F1-score, 0.870 precision, 0.838 recall for the second test dataset. The best model for classification achieved an average of 0.778 top-one, 0.890 top-two and 0.942 top-three accuracy, 0.798 F1-score, 0.766 recall and 0.838 precision for the first test dataset and 0.737 top-one, 0.906 top-two, 0.924 top-three, 0.729 F1-score, 0.676 recall and 0.790 precision for the second test dataset. The results demonstrated that the proposed ZSL is a promising tool for land cover mapping based on high-resolution photos.

Classical and Quantum Signatures of Quantum Phase Transitions in a (Pseudo) Relativistic Many-Body System

We identify a (pseudo) relativistic spin-dependent analogue of the celebrated quantum phase transition driven by the formation of a bright soliton in attractive one-dimensional bosonic gases. In this new scenario, due to the simultaneous existence of the linear dispersion and the bosonic nature of the system, special care must be taken with the choice of energy region where the transition takes place. Still, due to a crucial adiabatic separation of scales, and identified through extensive numerical diagonalization, a suitable effective model describing the transition is found. The corresponding mean-field analysis based on this effective model provides accurate predictions for the location of the quantum phase transition when compared against extensive numerical simulations. Furthermore, we numerically investigate the dynamical exponents characterizing the approach from its finite-size precursors to the sharp quantum phase transition in the thermodynamic limit.

Unravelling the metabolism black-box in a dynamic wetland environment using a hybrid model framework: Storm driven changes in oxygen budgets

Estimating gross primary production and ecosystem respiration from oxygen data is performed widely in aquatic systems, yet these estimates can be challenged by high advective fluxes of oxygen. In this study, we develop a hybrid framework linking data-driven and process-based modelling to examine the effect of storm events on oxygen budgets in a constructed wetland. After calibration against measured flow and water temperature data over a two-month period with three storm events, the model was successfully validated against high frequency dissolved oxygen (DO) data exhibiting large diurnal fluctuations. The results demonstrated that pulses of high-DO water injected into the wetland during storm events were able to dramatically change the wetland oxygen budget. A shift was observed in the dominant oxygen inputs, from benthic net production during non-storm periods, to inflows of oxygen during storm events, which served to dampen the classical diurnal oxygen signature. The model also demonstrated the changing balance of pelagic versus benthic production and hypoxia extent in response to storm events, which has implications for the nutrient attenuation performance of constructed wetlands. The study highlights the benefit of linking analysis of high-frequency oxygen data with process-based modelling tools to unravel the varied responses of components of the oxygen budget to storm events.

Signature invariants related to the unknotting number

New lower bounds on the unknotting number of a knot are constructed from the classical knot signature function. These bounds can be twice as strong as previously known signature bounds. They can also be stronger than known bounds arising from Heegaard Floer and Khovanov homology. Results include new bounds on the Gordian distance between knots and information about four-dimensional knot invariants. By considering a related non-balanced signature function, bounds on the unknotting number of slice knots are constructed; these are related to the property of double-sliceness.

Multi-Source and Multi-Temporal Image Fusion on Hypercomplex Bases

This article spanned a new, consistent framework for production, archiving, and provision of analysis ready data (ARD) from multi-source and multi-temporal satellite acquisitions and an subsequent image fusion. The core of the image fusion was an orthogonal transform of the reflectance channels from optical sensors on hypercomplex bases delivered in Kennaugh-like elements, which are well-known from polarimetric radar. In this way, SAR and Optics could be fused to one image data set sharing the characteristics of both: the sharpness of Optics and the texture of SAR. The special properties of Kennaugh elements regarding their scaling—linear, logarithmic, normalized—applied likewise to the new elements and guaranteed their robustness towards noise, radiometric sub-sampling, and therewith data compression. This study combined Sentinel-1 and Sentinel-2 on an Octonion basis as well as Sentinel-2 and ALOS-PALSAR-2 on a Sedenion basis. The validation using signatures of typical land cover classes showed that the efficient archiving in 4 bit images still guaranteed an accuracy over 90% in the class assignment. Due to the stability of the resulting class signatures, the fuzziness to be caught by Machine Learning Algorithms was minimized at the same time. Thus, this methodology was predestined to act as new standard for ARD remote sensing data with an subsequent image fusion processed in so-called data cubes.

Machine Learning to Identify Flexibility Signatures of Class A GPCR Inhibition.

We show that machine learning can pinpoint features distinguishing inactive from active states in proteins, in particular identifying key ligand binding site flexibility transitions in GPCRs that are triggered by biologically active ligands. Our analysis was performed on the helical segments and loops in 18 inactive and 9 active class A G protein-coupled receptors (GPCRs). These three-dimensional (3D) structures were determined in complex with ligands. However, considering the flexible versus rigid state identified by graph-theoretic ProFlex rigidity analysis for each helix and loop segment with the ligand removed, followed by feature selection and k-nearest neighbor classification, was sufficient to identify four segments surrounding the ligand binding site whose flexibility/rigidity accurately predicts whether a GPCR is in an active or inactive state. GPCRs bound to inhibitors were similar in their pattern of flexible versus rigid regions, whereas agonist-bound GPCRs were more flexible and diverse. This new ligand-proximal flexibility signature of GPCR activity was identified without knowledge of the ligand binding mode or previously defined switch regions, while being adjacent to the known transmission switch. Following this proof of concept, the ProFlex flexibility analysis coupled with pattern recognition and activity classification may be useful for predicting whether newly designed ligands behave as activators or inhibitors in protein families in general, based on the pattern of flexibility they induce in the protein.

Tracking Drug-Induced Epithelial-Mesenchymal Transition in Breast Cancer by a Microfluidic Surface-Enhanced Raman Spectroscopy Immunoassay.

Epithelial-mesenchymal transition (EMT) is a primary mechanism for cancer metastasis. Detecting the activation of EMT can potentially convey signs of metastasis to guide treatment management and improve patient survival. One of the classic signatures of EMT is characterized by dynamic changes in cellular expression levels of E-cadherin and N-cadherin, whose soluble active fragments have recently been reported to be biomarkers for cancer diagnosis and prognosis. Herein, a microfluidic immunoassay (termed "SERS immunoassay") based on sensitive and simultaneous detection of soluble E-cadherin (sE-cadherin) and soluble N-cadherin (sN-cadherin) for EMT monitoring in patients' plasma is presented. The SERS immunoassay integrates in situ nanomixing and surface-enhanced Raman scattering readout to enable accurate detection of sE-cadherin and sN-cadherin from as low as 10 cells mL-1 . This assay enables tracking of a concurrent decrease in sE-cadherin and increase in sN-cadherin in breast cancer cells undergoing drug-induced mesenchymal transformation. The clinical potential of the SERS immunoassay is further demonstrated by successful detection of sE-cadherin and sN-cadherin in metastatic stage IV breast cancer patient plasma samples. The SERS immunoassay can potentially sense the activation of EMT to provide early indications of cancer invasions or metastasis.

Quantum Dual Signature with Coherent States Based on Chained Phase-Controlled Operations

A novel encryption algorithm called the chained phase-controlled operation (CPCO) is presented in this paper, inspired by CNOT operation, which indicates a stronger correlation among message states and each message state depending on not only its corresponding key but also other message states and their associated keys. Thus, it can prevent forgery effectively. According to the encryption algorithm CPCO and the classical dual signature protocols, a quantum dual signature scheme based on coherent states is proposed in this paper. It involves three participants, the customer Alice, the merchant Bob and the bank Trent. Alice expects to send her order message and payment message to Bob and Trent, respectively. It is required that the two messages must be linked to guarantee the payment is paid for the corresponding order. Thus, Alice can generate a quantum dual signature to achieve the goal. In detail, Alice firstly signs her two messages with the shared secret key. Then She connects the two signatures into a quantum dual signature. Finally, Bob and Trent severally verify the signatures of the order message and the payment message. Security analysis shows that our scheme can ensure its security against forgery, repudiation and denial. In addition, simulation experiments based on the Strawberry Fields platform are performed to valid the feasibility of CPCO. Experimental results demonstrate that CPCO is viable and the expected coherent states can be acquired with high fidelity, which indicates that the encryption algorithm of the scheme can be implemented on quantum devices effectively.

Sedimentation history and depositional model of Palaeo-Mesoproterozoic Tadpatri Formation, Cuddapah Basin, India

The Tadpatri Formation belonging to the proterozoic intracratonic Cuddapah Basin, in an around Utakonda (15° 16′ 08.80″ N, 77° 52′ 01.77″ E), Shidhanagar-II (15° 13′ 38.72″ N, 77° 50′ 30.84″ E), and Pacharu Mekala Palli (15° 04′ 27.23″ N, 77° 50′ 29.44″ E) area, Andhra Pradesh, Southern India records well-developed successions of shale with lesser amount of stromatolitic dolomite, chert, sandstone, and felsic as well as mafic sill bodies. Three stratigraphic sections are measured. Based on sedimentological studies, 11 facies are identified from the studied area. The array of facies of Tadpatri Formation may be grouped into following facies associations: (a) inner ramp, (b) mid-ramp, and (c) outer ramp. Facies associations along with associated sedimentary structures and stromatolite morphology demonstrate that the Tadpatri Formation in the study area is deposited in a mud-dominant mixed clastic–carbonate ramp setting. Further comparison with age-equivalent formations suggests that the Tadpatri Formation bears many of the classic signatures of proterozoic mixed clastic–carbonate deposits.

Site Response Study and Amplification Factor for Shallow Bedrock Sites

Amplification is a key paramter considered to account modifcation of seimic wave in the soil for earthquake resistance design of structure placed on soil. Initially, earthquake wave amplifications are related with shear wave velocity (Vs) ratio of soil and foundation layer, and then it was related to average value of Vs up to 30 m (Vs30 ). Application of Vs30 concept to represent amplification in shallow bedrock sites is questionable and has rock velocity added to soil velocity. In this study, shallow bedrock sites in Bengaluru, Chennai, Coimbatore and Vishakhapatnam has been analyzed to understand amplifications and its proximity to Vs30 . The site response calculations are done using one-dimensional nonlinear approach. Intraplate recordings from around the world suitable for the study area are selected. For comparison with known data, the amplification factors are evaluated considering the period ranges similar to international codes. Then, considering spectral signatures from response spectra for all site rock and surface motions, the factors are calculated for period range 0.01–0.06 s and 0.05–1.0 s. Based on the study, only one mid-period amplification factor has been proposed. This amplification factor represents the significant amplification of the site. Acceleration spectra show similar trends for different site classes irrespective of the fact that profiles are selected based on Vs values or SPT-N values. This is observed over a wide range of results and indicates shear modulus as an influencing parameter. Comparison of spectral signatures for different site classes suggests that amplification reduces as the modulus of the soil column increases. Thus, it may be appropriate to classify sites based on shear modulus of soil column.

A novel classifier for multivariate instance using graph class signatures

Applications like identifying different customers from their unique buying behaviours, determining ratings of a product given by users based on different sets of features, etc. require classification using class-specific subsets of features. Most of the existing state-of-the-art classifiers for multivariate data use complete feature set for classification regardless of the different class labels. Decision tree classifier can produce class-wise subsets of features. However, none of these classifiers model the relationship between features which may enhance classification accuracy. We call the class-specific subsets of features and the features’ interrelationships as class signatures. In this work, we propose to map the original input space of multivariate data to the feature space characterized by connected graphs as graphs can easily model entities, their attributes, and relationships among attributes. Mostly, entities are modeled using graphs, where graphs occur naturally, for example, chemical compounds. However, graphs do not occur naturally in multivariate data. Thus, extracting class signatures from multivariate data is a challenging task. We propose some feature selection heuristics to obtain class-specific prominent subgraph signatures. We also propose two variants of class signatures based classifier namely: 1) maximum matching signature (gMM), and 2) score and size of matched signatures (gSM). The effectiveness of the proposed approach on real-world and synthetic datasets has been studied and compared with other established classifiers. Experimental results confirm the ascendancy of the proposed class signatures based classifier on most of the datasets.

An original method for tree species classification using multitemporal multispectral and hyperspectral satellite data

This study proposes an original method for tree species classification by satellite remote sensing. The method uses multitemporal multispectral (Landsat OLI) and hyperspectral (Resurs-P) data acquired from determined vegetation periods. The method is based on an original database of spectral features taking into account seasonal variations of tree species spectra. Changes in the spectral signatures of forest classes are analyzed and new spectral–temporal features are created for the classification. Study sites are located in the Czech Republic and northwest (NW) Russia. The differences in spectral reflectance between tree species are shown as statistically significant in the sub-seasons of spring, first half of summer, and main autumn for both study sites. Most of the errors are related to the classification of deciduous species and misclassification of birch as pine (NW Russia site), pine as mixture of pine and spruce, and pine as mixture of spruce and beech (Czech site). Forest species are mapped with accuracy as high as 80% (NW Russia site) and 81% (Czech site). The classification using multitemporal multispectral data has a kappa coefficient 1.7 times higher than does that of classification using a single multispectral image and 1.3 times greater than that of the classification using single hyperspectral images. Potentially, classification accuracy can be improved by the method when applying multitemporal satellite hyperspectral data, such as in using new, near-future products EnMap and/or HyspIRI with high revisit time.

НОВИЙ ПІДХІД ДО ПОБУДОВИ ПОСТ-КВАНТОВОЇ СХЕМИ ЕЛЕКТРОННОГО ЦИФРОВОГО ПІДПИСУ

Cryptographic information security techniques are essential in building a modern cybersecurity infrastructure. Recently, there have been new challenges and threats to cryptographic transformation. In particular, the emergence and rapid development of the latest quantum computing technologies necessitates the urgent need for the development and research of new methods of post-quantum cryptographic transformations, that is, those that will be sustainable even if quantum cryptanalysis is possible. This article is devoted to the analysis of possibilities of implementation of digital signature schemes based on using error-correcting codes. This approach allows cryptographers to build schemes that are resistant to both classic cryptanalysis and cryptanalysis which uses quantum computing. The article describes the principles of the classic digital signature scheme which is named CFS and built using a Niederreiter-like transform, and also we propose a new approach that enables an implementation of signature according to the McEliece transformations. This approach preserves the advantages of its predecessor and provides additional protection against special attacks. Also, a comparative analysis and characterization of the considered schemes according to the criteria of resistance to classic and quantum cryptanalysis, complexity of necessary transformations and length of generated signatures are made. The results show that reliable and secure cryptographic transformations can be built, in particular, electronic digital signature algorithms that are code-based and secure even in the case of quantum cryptanalysis. However, it should be noted that the drawback of code-based signature schemes is the large amount of key data required by the algorithm, as well as the difficulty in creating a signature due to the need for multiple decryption of the syndrome, which remains a topical topic and needs further research

Public-key cryptosystem based on quantum BCH codes and its quantum digital signature

There is a security threat to the traditional cryptography because of the emergence of quantum computation. Quantum computers can break through many cryptosystems based on mathematical puzzles. The post-cryptography has been established for the sake of the security. It has been proved that McEliece public-key cryptosystem based on error-correcting codes can resist quantum attacks. In this paper, several new generating algorithms of the quantum Bose-Chaudhuri-Hocquenghem (BCH) codes have been proposed, and the quantum BCH codes can correct the errors including bit-flip and phase-flip. At the same time, the quantum McEliece public-key cryptosystem and quantum Niederreiter public key cryptosystem have been designed by us on the basis of quantum BCH codes in this paper, and their encryption and decryption procedures are created in detail. Our cryptosystem not only retains the advantages of postquantum computation, but also can encrypt or decrypt in quantum state. From the perspective of computational complexity theory, both cryptosystems can resist the attacks of Shor algorithm and Grover algorithm very well. The Niederreiter classical digital signature is designed by us according to the structure characteristics of quantum BCH codes.

Quantum material topology via defect engineering

Current approaches of discovering new topological quantum materials, such as (magnetic) Weyl materials, by screening material databases or by rational design suffer from experimental challenges in growing the material in the desired geometry, hampering their potential applications. We take an approach of defect-engineering a topology starting from systems with experimentally well-controlled growth. Using SnTe, an experimentally viable topological crystalline insulator, as our model system, we elucidate the different effects of breaking time-reversal, inversion, and mirror symmetries using magnetic dopant defects, to controllably induce a topological phase transition. To this effect, we compute the thermodynamics, magnetism, and band-structure topology of magnetically doped SnTe using a full ab initio approach. In this process, we have discovered that Cr-doped SnTe is a Weyl semimetal. We have computed its classic Fermi-arc experimental signature on the (001) surface and also showed that it has a large anamolous Hall conductivity (AHC) of $\ensuremath{\sim}250 {\mathrm{\ensuremath{\Omega}}}^{\ensuremath{-}1}\phantom{\rule{0.16em}{0ex}}{\text{cm}}^{\ensuremath{-}1}$. We predict a large Curie temperature (${T}_{c}$) of 62 K even at $3.3%$ doping, with both ${T}_{c}$ and AHC being tunable with Cr concentration, suggesting potential for room-temperature applications. Our study in general paves the way for defect-designing room-temperature topological quantum phases.

Identity-based quantum signature scheme with strong security

Recently, Chen et al. proposed an identity-based quantum signature scheme (IDQSS). Their scheme has the advantages of the classical identity-based signature (IDS) scheme. However, Chen et al.’s scheme cannot resist against the private key generator (PKG)’s forgery attack. On the other hand, in their scheme, many rounds of quantum swap tests have to be performed by the verifier during the signature verification phase. To improve the security and efficiency of the IDQSS, a new IDQSS is proposed. In our scheme, the private key of the signer is generated by PKG according to the signer’s identity, which is used as the corresponding public key. The signer generates a quantum signature with her private key and the secret parameters shared with the verifier. Anyone can verify the quantum signature with the identity of the signer. Our scheme overcomes the security drawback of Chen et al.’s scheme and inherits the merits of the classical IDS system. It has the security properties such as information-theoretical security, non-repudiation and unforgeability. At the same time, in our scheme, the verifier need not perform any quantum swap test. Therefore, our IDQSS is more secure and efficient.

Neurobehavioral effects of selected tobacco constituents in rodents following subchronic administration

Bidirectional correlations between cigarette smoking and affective disorders, such as depression, anxiety, and schizophrenia, are well documented. These findings have led to substantial investigations into the effects of the major tobacco alkaloid, nicotine, and to a lesser extent, of other tobacco constituents, on the central nervous system (CNS). However, systematic profiling of the neuropharmacological effects of tobacco constituents is limited. To elucidate the effects of selected tobacco constituents on the CNS, we used the SmartCube® system, which captures and classifies behavioral features of compound-treated mice, to profile the psychiatric drugs-like properties of previously reported neuroactive tobacco compounds in mice. Daily intraperitoneal injection of nicotine (0.5 and 1 mg/kg/day) and anatabine (5 mg/kg/day) for 7 days produced antidepressant-like behavioral SmartCube® signatures in mice, and these results were supported by the improved active coping responses in the forced swim tests. Conversely, ferulic acid did not show any identifiable class signatures in the SmartCube® tests, but rather displayed subclass signatures associated with acetylcholinesterase inhibitors. In novel object recognition memory test in rats, ferulic acid improved memory after 7 days of subcutaneous injection at 0.3 or 3 mg/kg/day. These results support previous findings showing the antidepressant drug-like effects of nicotine and the nootropic effects of ferulic acid. This is also the first report on the antidepressant drug-like effects of anatabine in rodents. This study provides a systemic behavioral evaluation of tobacco alkaloids and further insights into the association between affective disorders and smoking incidence.

Takeda G-protein Receptor (TGR)-5 Evolves Classical Activestate Conformational Signatures in Complex with Chromolaena Odorata-derived Flavonoid-5,7-dihydroxy-6-4-dimethoxyflavanone

Background: Takeda G-protein receptor 5 (TGR5) via glucagon-like peptide release and insulin signaling underlies antidiabetic roles of TGR5 agonists. Chromolaena Odorata- derived flavonoid-5,7-dihydroxy-6-4-dimethoxyflavanone (COF) has been identified as (TGR5) agonist. The structural basis for their interaction has not been studied. Objective: This study aimed at providing both structural and dynamic insights into COF/TGR5 interaction. Methods: Classical GPCR activation signatures (TMIII-TMVI ionic lock, toggle switches, internal water pathway) using classical MD simulation have been used. Results: Y893.29, N933.33 and E1695.43 are key residues found to be involved in ligand binding; the continuous internal water pathway connects hydrophilic groups of the ligand to the TMIII-TMVI interface in COF-bound state, TMIII-TMVI ionic locks ruptures in COF-TGR5 complex but not antagonist-bound state, and ruptured ionic lock is associated with the evolution of active-state “VPVAM” (analogous to “NPxxY”) conformation. Dihedral angles (c2) calculated along the trajectory strongly suggest W2376.48 as a ligand-dependent toggle switch. Conclusion: TGR5 evolves active state conformation from a starting intermediate state conformation when bound to COF, which further supports its underlying anti-diabetic activities.