Correction: Kumar et al. Face Spoofing, Age, Gender and Facial Expression Recognition Using Advance Neural Network Architecture-Based Biometric System. Sensors 2022, 22, 5160.

In the original publication [...].

Novel tricyclic small molecule inhibitors of Nicotinamide N-methyltransferase for the treatment of metabolic disorders.

Nicotinamide N-methyltransferase (NNMT) is a metabolic regulator that catalyzes the methylation of nicotinamide (Nam) using the co-factor S-adenosyl-L-methionine to form 1-methyl-nicotinamide (MNA). Overexpression of NNMT and the presence of the active metabolite MNA is associated with a number of diseases including metabolic disorders. We conducted a high-throughput screening campaign that led to the identification of a tricyclic core as a potential NNMT small molecule inhibitor series. Elaborate medicinal chemistry efforts were undertaken and hundreds of analogs were synthesized to understand the structure activity relationship and structure property relationship of this tricyclic series. A lead molecule, JBSNF-000028, was identified that inhibits human and mouse NNMT activity, reduces MNA levels in mouse plasma, liver and adipose tissue, and drives insulin sensitization, glucose modulation and body weight reduction in a diet-induced obese mouse model of diabetes. The co-crystal structure showed that JBSNF-000028 binds below a hairpin structural motif at the nicotinamide pocket and stacks between Tyr-204 (from Hairpin) and Leu-164 (from central domain). JBSNF-000028 was inactive against a broad panel of targets related to metabolism and safety. Interestingly, the improvement in glucose tolerance upon treatment with JBSNF-000028 was also observed in NNMT knockout mice with diet-induced obesity, pointing towards the glucose-normalizing effect that may go beyond NNMT inhibition. JBSNF-000028 can be a potential therapeutic option for metabolic disorders and developmental studies are warranted.

Face Spoofing, Age, Gender and Facial Expression Recognition Using Advance Neural Network Architecture-Based Biometric System.

Nowadays, the demand for soft-biometric-based devices is increasing rapidly because of the huge use of electronics items such as mobiles, laptops and electronic gadgets in daily life. Recently, the healthcare department also emerged with soft-biometric technology, i.e., face biometrics, because the entire data, i.e., (gender, age, face expression and spoofing) of patients, doctors and other staff in hospitals is managed and forwarded through digital systems to reduce paperwork. This concept makes the relation friendlier between the patient and doctors and makes access to medical reports and treatments easier, anywhere and at any moment of life. In this paper, we proposed a new soft-biometric-based methodology for a secure biometric system because medical information plays an essential role in our life. In the proposed model, 5-layer U-Net-based architecture is used for face detection and Alex-Net-based architecture is used for classification of facial information i.e., age, gender, facial expression and face spoofing, etc. The proposed model outperforms the other state of art methodologies. The proposed methodology is evaluated and verified on six benchmark datasets i.e., NUAA Photograph Imposter Database, CASIA, Adience, The Images of Groups Dataset (IOG), The Extended Cohn-Kanade Dataset CK+ and The Japanese Female Facial Expression (JAFFE) Dataset. The proposed model achieved an accuracy of 94.17% for spoofing, 83.26% for age, 95.31% for gender and 96.9% for facial expression. Overall, the modification made in the proposed model has given better results and it will go a long way in the future to support soft-biometric based applications.

Efficient 3D AlexNet Architecture for Object Recognition Using Syntactic Patterns from Medical Images.

In computer vision and medical image processing, object recognition is the primary concern today. Humans require only a few milliseconds for object recognition and visual stimulation. This led to the development of a computer-specific pattern recognition method in this study for identifying objects in medical images such as brain tumors. Initially, an adaptive median filter is used to remove the noise from MRI images. Thereafter, the contrast image enhancement technique is used to improve the quality of the image. To evaluate the wireframe model, the cellular logic array processing (CLAP)-based algorithm is then applied to images. The basic patterns of three-dimensional (3D) images are then identified from the input image by scanning the whole image. The frequency of these patterns is also used for object classification. A deep neural network is then utilized for the classification of brain tumor. In the proposed model, the syntactic pattern recognition technique is used to find the feature vector and 3D AlexNet is used for brain tumor classification. To evaluate the performance of the proposed work, three benchmark brain tumor datasets are used, i.e., Figshare, Brain MRI Kaggle, and Medical MRI datasets and BraTS 2019 dataset. The comparative analyses reveal that the proposed brain tumor classification model achieves significantly better performance than the existing models.

Object-Based Image Retrieval Using the U-Net-Based Neural Network.

Day by day, all the research communities have been focusing on digital image retrieval due to more internet and social media uses. In this paper, a U-Net-based neural network is proposed for the segmentation process and Haar DWT and lifting wavelet schemes are used for feature extraction in content-based image retrieval (CBIR). Haar wavelet is preferred as it is easy to understand, very simple to compute, and the fastest. The U-Net-based neural network (CNN) gives more accurate results than the existing methodology because deep learning techniques extract low-level and high-level features from the input image. For the evaluation process, two benchmark datasets are used, and the accuracy of the proposed method is 93.01% and 88.39% on Corel 1K and Corel 5K. U-Net is used for the segmentation purpose, and it reduces the dimension of the feature vector and feature extraction time by 5 seconds compared to the existing methods. According to the performance analysis, the proposed work has proven that U-Net improves image retrieval performance in terms of accuracy, precision, and recall on both the benchmark datasets.

High-Affinity Alkynyl Bisubstrate Inhibitors of Nicotinamide N-Methyltransferase (NNMT).

Nicotinamide N-methyltransferase (NNMT) is a metabolic enzyme that methylates nicotinamide (NAM) using cofactor S-adenosylmethionine (SAM). NNMT overexpression has been linked to diabetes, obesity, and various cancers. In this work, structure-based rational design led to the development of potent and selective alkynyl bisubstrate inhibitors of NNMT. The reported nicotinamide-SAM conjugate (named NS1) features an alkyne as a key design element that closely mimics the linear, 180° transition state geometry found in the NNMT-catalyzed SAM → NAM methyl transfer reaction. NS1 was synthesized in 14 steps and found to be a high-affinity, subnanomolar NNMT inhibitor. An X-ray cocrystal structure and SAR study revealed the ability of an alkynyl linker to span the methyl transfer tunnel of NNMT with ideal shape complementarity. The compounds reported in this work represent the most potent and selective NNMT inhibitors reported to date. The rational design principle described herein could potentially be extended to other methyltransferase enzymes.

Tauroursodeoxycholic acid (TUDCA) attenuates pressure overload-induced cardiac remodeling by reducing endoplasmic reticulum stress.

Pressure overload in the heart induces pathological hypertrophy and is associated with cardiac dysfunction. Apoptosis and fibrosis signaling initiated by the endoplasmic reticulum stress (ERS) is known to contribute to these maladaptive effects. The aim of this study was to investigate whether reduction of ERS by a known chemical chaperone, tauroursodeoxycholic acid (TUDCA) can attenuate pressure overload-induced cardiac remodeling in a mouse model of transverse aortic constriction (TAC). Oral administration of TUDCA at a dose of 300 mg/kg body weight (BW) in the TUDCA-TAC group reduced ERS markers (GRP78, p-PERK, and p-eIf2α), compared to the Vehicle (Veh)-TAC group. TUDCA administration, for 4 weeks after TAC significantly reduced cardiac hypertrophy as shown by the reduced heart weight (HW) to BW ratio, and expression of hypertrophic marker genes (ANF, BNP, and α-SKA). Masson's trichrome staining showed that myocardial fibrosis and collagen deposition were also significantly reduced in the TUDCA-TAC group. We also found that TUDCA significantly decreased expression of TGF-ß signaling proteins and collagen isoforms. TUDCA administration also reduced cardiac apoptosis and the related proteins in the TUDCA-TAC group. Microarray analysis followed by gene ontology (GO) and pathway analysis demonstrated that extracellular matrix genes responsible for hypertrophy and fibrosis, and mitochondrial genes responsible for apoptosis and fatty acid metabolism were significantly altered in the Veh-TAC group, but the alterations were normalized in the TUDCA-TAC group, suggesting potential of TUDCA in treatment of heart diseases related to pressure-overload.

Salubrinal Alleviates Pressure Overload-Induced Cardiac Hypertrophy by Inhibiting Endoplasmic Reticulum Stress Pathway.

Pathological hypertrophy of the heart is closely associated with endoplasmic reticulum stress (ERS), leading to maladaptations such as myocardial fibrosis, induction of apoptosis, and cardiac dysfunctions. Salubrinal is a known selective inhibitor of protein phosphatase 1 (PP1) complex involving dephosphorylation of phospho-eukaryotic translation initiation factor 2 subunit (p-eIF2)-α, the key signaling process in the ERS pathway. In this study, the effects of salubrinal were examined on cardiac hypertrophy using the mouse model of transverse aortic constriction (TAC) and cell model of neonatal rat ventricular myocytes (NRVMs). Treatment of TAC-induced mice with salubrinal (0.5 mg·kg-1·day-1) alleviated cardiac hypertrophy and tissue fibrosis. Salubrinal also alleviated hypertrophic growth in endothelin 1 (ET1)-treated NRVMs. Therefore, the present results suggest that salubrinal may be a potentially efficacious drug for treating pathological cardiac remodeling.

Characterization of Ca(2+)-Dependent Protein-Protein Interactions within the Ca(2+) Release Units of Cardiac Sarcoplasmic Reticulum.

In the heart, excitation-contraction (E-C) coupling is mediated by Ca(2+) release from sarcoplasmic reticulum (SR) through the interactions of proteins forming the Ca(2+) release unit (CRU). Among them, calsequestrin (CSQ) and histidine-rich Ca(2+) binding protein (HRC) are known to bind the charged luminal region of triadin (TRN) and thus directly or indirectly regulate ryanodine receptor 2 (RyR2) activity. However, the mechanisms of CSQ and HRC mediated regulation of RyR2 activity through TRN have remained unclear. We first examined the minimal KEKE motif of TRN involved in the interactions with CSQ2, HRC and RyR2 using TRN deletion mutants and in vitro binding assays. The results showed that CSQ2, HRC and RyR2 share the same KEKE motif region on the distal part of TRN (aa 202-231). Second, in vitro binding assays were conducted to examine the Ca(2+) dependence of protein-protein interactions (PPI). The results showed that TRN-HRC interaction had a bell-shaped Ca(2+) dependence, which peaked at pCa4, whereas TRN-CSQ2 or TRN-RyR2 interaction did not show such Ca(2+) dependence pattern. Third, competitive binding was conducted to examine whether CSQ2, HRC, or RyR2 affects the TRN-HRC or TRN-CSQ2 binding at pCa4. Among them, only CSQ2 or RyR2 competitively inhibited TRN-HRC binding, suggesting that HRC can confer functional refractoriness to CRU, which could be beneficial for reloading of Ca(2+) into SR at intermediate Ca(2+) concentrations.

IPAVS: Integrated Pathway Resources, Analysis and Visualization System.

Integrated Pathway Resources, Analysis and Visualization System (iPAVS) is an integrated biological pathway database designed to support pathway discovery in the fields of proteomics, transcriptomics, metabolomics and systems biology. The key goal of IPAVS is to provide biologists access to expert-curated pathways from experimental data belonging to specific biological contexts related to cell types, tissues, organs and diseases. IPAVS currently integrates over 500 human pathways (consisting of 24, 574 interactions) that include metabolic-, signaling- and disease-related pathways, drug-action pathways and several large process maps collated from other pathway resources. IPAVS web interface allows biologists to browse and search pathway resources and provides tools for data import, management, visualization and analysis to support the interpretation of biological data in light of cellular processes. Systems Biology Graphical Notations (SBGN) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway notations are used for the visual display of pathway information. The integrated datasets in IPAVS are made available in several standard data formats that can be downloaded. IPAVS is available at: http://ipavs.cidms.org.

Akt contributes to activation of the TRIF-dependent signaling pathways of TLRs by interacting with TANK-binding kinase 1.

Toll/IL-1R domain-containing adaptor inducing IFN-ß (TRIF) is an adaptor molecule that is recruited to TLR3 and -4 upon agonist stimulation and triggers activation of IFN regulatory factor 3 (IRF3) and expression of type 1 IFNs, which are critical for cellular antiviral responses. We show that Akt is a downstream molecule of TRIF/TANK-binding kinase 1 (TBK1) and plays an important role in the activation of IRF3 by TLR3 and -4 agonists. Blockade of Akt by a dominant-negative mutant or by short interfering RNA decreased IRF3 activation and IFN-ß expression induced by polyinosinic:polycytidylic acid [poly(I:C)], LPS, TRIF, and TBK1. Association of endogenous TBK1 and Akt was observed in macrophages when stimulated with poly(I:C) and LPS. In vitro kinase assays combined with reversed-phase liquid chromatography mass spectrometry analysis showed that TBK1 enhanced phosphorylation of Akt on Ser(473), whereas knockdown of TBK1 expression by short interfering RNA in macrophages decreased poly(I:C)- and LPS-induced Akt phosphorylation. Embryonic fibroblasts derived from TBK1 knockout mice also showed impaired Akt phosphorylation in response to poly(I:C) and LPS. To our knowledge, our results demonstrate a new regulatory mechanism for Akt activation mediated by TBK1 and a novel role of Akt in TLR-mediated immune responses.