Sulfonated Poly(2,6-dimethyl-1,4-phenylene ether)-Modified Mixed-Matrix Bifunctional Polyelectrolyte Membranes for Long-Run Anthrarufin-Based Redox Flow Batteries.

The resurgence in designing polyelectrolyte membrane (PEM) materials has propound grid-scale electrochemical energy storage devices. Herein, we report on studies corroborating the synergistic influence of ionic domain microstructure modification and intercalation of telechelic bis-piperidinium-functionalized graphene oxide (GO) to fabricate stable bifunctional membranes from sulfonated poly(2,6-dimethyl-1,4-phenylene ether) (sPPE) for efficient anthrarufin-based alkaline redox flow batteries. A critically long-lasting quest on alkaline stability and -OH conductivity dilemma in hydrocarbon-based PEMs is meticulously resolved via a bifunctional ion-conducting matrix. Preferential studies on hydrophilic domain distribution in sPPE suggest that, with high microphase homogeneity, higher specific capacity retentions are achievable during galvanostatic charge-discharge (GCD) analysis. Moreover, the low-capacity issues were overcome by improving the redoxolyte-membrane interface affinities incorporating bis-piperidinium-bearing graphene oxide (bis-QGO). Consequently, at 1.0 and 2.0 wt % intercalation of bis-QGO, the bifunctional polyelectrolyte membranes (BFPMs) impart lowest overpotentials of 93 mV (for BFPM-1.0) and ∼100 mV (for BFPM-2.0) which are ∼43 and 40% lower than that of Nafion-117 (i.e., ∼164 mV). Furthermore, the efficiency of BFPMs, viz., the Coulombic, voltage, and energy efficiencies, was ∼95-98%, ∼85%, and ≥80% at 20 mA cm-2, respectively. In long-cycling operations, the GCD profile evidenced ∼99% efficiency retention over 450 cycles and illustrated reproducible rate capability. Finally, the polarization studies of BFPMs revealed ∼54% higher peak power density (87.5 mW cm-2) delivery than Nafion-117 (∼57 mW cm-2). We believe that this strategic designing approach could offer newer and simple avenues to avail high-performance BFPMs at low intercalation loads for alkaline electrochemical energy storage and related applications.

Isolation and chemical characterization of the biosurfactant produced by Gordonia sp. IITR100.

Biosurfactants are amphipathic molecules produced from microorganisms. There are relatively few species known where the detailed chemical characterization of biosurfactant has been reported. Here, we report isolation and chemical characterization of the biosurfactant produced by a biodesulfurizing bacterium Gordonia sp. IITR100. Biosurfactant production was determined by performing oil spreading, drop-collapse, Emulsion index (E24), and Bacterial adhesion to hydrocarbons (BATH) assay. The biosurfactant was identified as a glycolipid by LCMS and GCMS analysis. The chemical structure was further confirmed by performing FTIR and NMR of the extracted biosurfactant. The emulsion formed by the biosurfactant was found to be stable between temperatures of 4°C to 30°C, pH of 6 to 10 and salt concentrations up to 2%. It was successful in reducing the surface tension of the aqueous media from 61.06 mN/m to 36.82 mN/m. The biosurfactant produced can be used in petroleum, detergents, soaps, the food and beverage industry and the healthcare industry.

iSignDB: A database for smartphone signature biometrics.

The signature has long been in use for the user verification. These signatures have user specific features that differentiate the individual for authentication. The signature verification can be offline or online. The offline verification considers only the static features of the signatures through the signature image, while the online verification considers various dynamic features associated with the signature such as pen pressure, pen tilt angle, velocity, acceleration, pen up and pen down, etc at various time stamps which are recorded using special digitizing tablets such as Wacom devices (STU-500, STU-530 and DTU-1031) [1,14] etc. In todays scenario, smartphones are widely used world-wide, and come equipped with various sensors e.g. accelerometer, gyroscope, magnetometer, GPS, etc. able to capture sensor logs and have been used widely in the literature to capture the dynamics of users' behaviour while a signer signs on his smartphone. However, there is scarcity of publicly available databases for the online signatures collected using smartphone. In the present work, we describe biometric signature dataset iSignDB captured using smartphone. The iSignDB [6,10] consists of the genuine signature samples of a user as well as the skilled forgery samples where imposter was given multiple attempts to mimic the mannerisms of the original signer before giving skilled forgery samples. A total of 30 samples towards the genuine signature over 3 sessions with 10 samples per session while 15 samples of the skilled forgery with 5 samples per session were collected. Each of the session were at least 15 days apart. The iOS and Android based smartphones (namely iPhone7 and Redmi Note 7) were used for the data collection. The sensors used to collect this data, present in the smartphone are the gyroscope, magnetometer, GPS, and accelerometer. Smartphones having sensors any one lesser than these four, were not used for data collection, in order to have consistent number of features under each sample. They generate the following sensor readings: angular velocity, acceleration, orientation, geomagnetic field in the x, y, and z directions, position, which is collected using the MATLAB Mobile App installed in the smartphone, that sends the data to a licensed MathWorks cloud account in the form of a multitude of sensor logs. Each sample has image of the signature along with sensor readings. Some of the publicly available smartphone biometric signature databases are DooDB [2], MOBISIG [3], eBioSign DS 2 [7], etc. in which at least acceleration sensor reading is present but the iSignDB ensures these five of the sensor readings (acceleration, angular velocity, magnetic field, orientation, position) under each sample. This dataset can be successfully used to design smartphone biometric signature authentication system which is robust against a number of spoof attacks [11], [12], [13], [14]. As every user has a unique way of handling his/her smartphone which varies over different level of emotional intelligence of the user over a time period, this dataset can also be used for behavioural analysis of the users.

A Small Molecule Inhibitor of VE-PTP Activates Tie2 in Schlemm's Canal Increasing Outflow Facility and Reducing Intraocular Pressure.

Purpose: Tyrosine kinase with immunoglobulin-like and EGF-like domains 2 (Tie2) activation in Schlemm's canal (SC) endothelium is required for the maintenance of IOP, making the angiopoietin/Tie2 pathway a target for new and potentially disease modifying glaucoma therapies. The goal of the present study was to examine the effects of a Tie2 activator, AKB-9778, on IOP and outflow function. Methods: AKB-9778 effects on IOP was evaluated in humans, rabbits, and mice. Localization studies of vascular endothelial protein tyrosine phosphatase (VE-PTP), the target of AKB-9778 and a negative regulator of Tie2, were performed in human and mouse eyes. Mechanistic studies were carried out in mice, monitoring AKB-9778 effects on outflow facility, Tie2 phosphorylation, and filtration area of SC. Results: AKB-9778 lowered IOP in patients treated subcutaneously for diabetic eye disease. In addition to efficacious, dose-dependent IOP lowering in rabbit eyes, topical ocular AKB-9778 increased Tie2 activation in SC endothelium, reduced IOP, and increased outflow facility in mouse eyes. VE-PTP was localized to SC endothelial cells in human and mouse eyes. Mechanistically, AKB-9778 increased the filtration area of SC for aqueous humor efflux in both wild type and in Tie2+/- mice. Conclusions: This is the first report of IOP lowering in humans with a Tie2 activator and functional demonstration of its action in remodeling SC to increase outflow facility and lower IOP in fully developed mice. Based on these studies, a phase II clinical trial is in progress to advance topical ocular AKB-9778 as a first in class, Tie2 activator for treatment for ocular hypertension and glaucoma.

High formin binding protein 17 (FBP17) expression indicates poor differentiation and invasiveness of ductal carcinomas.

Formin binding protein 17 (FBP17) belongs to Cdc-42 interacting protein 4 subfamily of F-BAR proteins. Recently, we had reported that FBP17 was overexpressed in invasive breast cancer cells and interacts with the actin regulatory proteins. We also reported that FBP17 promotes invadopodia formation and enhances extracellular matrix degradation. The current study determines FBP17 expression in invasive ductal carcinomas (IDCs) using breast cancer tissue microarrays (TMAs) (82 IDCs with variable receptor status and 8 Normal adjacent tissues) and its correlation with the clinico-pathological features. Immunohistochemistry of human breast cancer TMAs showed the significant elevation in the levels of FBP17 in breast cancer tissues than the normal (p ≤ 0.0001). Interestingly, FBP17 had a higher expression in invasive molecular subtypes HER2 and TNBC (p ≤ 0.05). Similarly, tumors with lymph node positive status showed elevated FBP17 expression in HER2 and TNBC subtypes (p ≤ 0.05). Surprisingly, grade 3 tumors demonstrated higher FBP17 expression (p ≤ 0.01) indicating its role in poorly differentiated tumors. Together, the data demonstrates the overexpression of FBP17 in invasive and poorly differentiated tumors. Understanding the role of FBP17 in poor differentiation and invasion of tumors in molecular subtypes at various level might represent as a potential molecular target against the disease.

A bacterial phyla dataset for protein function prediction.

Protein function prediction has been the most worked upon and the most challenging problem for computational biologists. The vast majority of known proteins have yet not been characterised experimentally, and there is significant gap between their structures and functions. New un-annotated sequences are being added to the public protein databases (e.g. UniprotKB) at an enormous pace [1]. Such proteins with unknown functions might play key role in the metabolism, growth and development regulation. Thus, if functions of unknown proteins left undiscovered, researchers may skip important information(s). Based on their sequence, structure, evolutionary history, and their association with other proteins, tools of computational biology can provide insights into the function of proteins [2]. For proteins with well characterised close relatives, it is trivial to infer function. Orphan proteins without discernible sequence relatives present a greater challenge [3]. Here the task of experimental characterisation is blind and becomes unwieldy. It is highly unlikely that all known proteins will ever be completely experimentally characterised [4]. Thus, there is an emergent need to develop fast and accurate computational approaches to fulfil this requirement. Towards this end, we prepared a dataset for protein function prediction by extracting protein sequences and annotations of reviewed prokaryotic proteins (total count 323,719 as accessed on date March 10, 2019) belonging to 9 bacterial phyla Actinobacteria, Bacteroidetes, Chlamydiae, Cyanobacteria, Firmicutes, Fusobacteria, Proteobacteria, Spirochaetes and Tenericutes. Corresponding to the most frequent 1739 Gene Ontology (Molecular Function) terms, samples were filtered, and 171,212 proteins were retrieved for feature generation. The Dataset was generated by calculating the sequence, sub-sequence, physiochemical, annotation-based features for each 171,212 reviewed proteins using method in [10]. These features constitute a total of 9890 attributes for each sequence of protein along with 1739 Gene Ontology terms. Each protein sequence is assigned one or more of 1739 Gene Ontology (Molecular Function) term as its target label. The Dataset contains the Entry and Entry name of each sequence corresponding to UniprotKB Database. This dataset being huge in size (171,212 samples X 9890 features, 1739 classes with multiple values) and equipped with enough number of positive and negative samples of each 1739 class, is good for testing efficiency of any upcoming deep learning models [5]. We divided the full dataset of 171,212 reviewed proteins in the ratio 3:1 to form Train/Test dataset 1; train dataset with 128,409 samples and test dataset with 42,803 samples to facilitate training of a deep learning model. The train and test datasets are stratified to contain good proportion of each 1739 classes. We then prepared a dataset 2 of pathogenic unreviewed proteins of the 9 bacterial phyla each with 9890 features same as train/train dataset of reviewed proteins but without target labels in order to predict their functions using deep learning model proposed in [5].

KIBRA Team Up with Partners to Promote Breast Cancer Metastasis.

Among women, breast cancer is the most frequently diagnosed cancer. Most of the breast cancers represent metastasis to distant organs at the time of diagnosis and accounts for the majority of deaths. Metastasis is characterized by many genetic aberrations including mutations, overexpression of oncogenes etc. KIBRA (KIdney/BRAin protein), a scaffolding protein is recently described as an important player in the process of invasion and metastasis. The Kidney/BRAin protein through its different domains interacts with various proteins to couple cytoskeleton arrangement, cell polarity and migration. N terminal and C terminal of the protein contains the WW, Internal C2 & putative class III PDZ domain that interacts with DDR1, DLC1 & PKCζ. These protein-protein interactions equip the breast cancer cells to invade and metastasize. Here, we discuss a comprehensive knowledge about the KIBRA protein, its domains and the interacting partners involved in metastasis of breast cancer.

A deep learning ensemble for function prediction of hypothetical proteins from pathogenic bacterial species.

Protein function prediction is a crucial task in the post-genomics era due to their diverse irreplaceable roles in a biological system. Traditional methods involved cost-intensive and time-consuming molecular biology techniques but they proved to be ineffective after the outburst of sequencing data through the advent of cost-effective and advanced sequencing techniques. To manage the pace of annotation with that of data generation, there is a shift to computational approaches which are based on homology, sequence and structure-based features, protein-protein interaction networks, phylogenetic profiles, and physicochemical properties, etc. A combination of these features has proven to be promising for protein function prediction in terms of improving prediction accuracy. In the present work, we have employed a combination of features based on sequence, physicochemical property, subsequence and annotation features with a total of 9890 features extracted and/or calculated for 171,212 reviewed prokaryotic proteins of 9 bacterial phyla from UniProtKB, to train a supervised deep learning ensemble model with the aim to categorize a bacterial hypothetical/unreviewed protein's function into 1739 GO terms as functional classes. The proposed system being fully dedicated to bacterial organisms is a novel attempt amongst various existing machine learning based protein function prediction systems based on mixed organisms. Experimental results demonstrate the success of the proposed deep learning ensemble model based on deep neural network method with F1 measure of 0.7912 on the prepared Test dataset 1 of reviewed proteins.

Effect of TiO2 and Fe doped TiO2 nanoparticles on mitochondrial membrane potential in HBL-100 cells.

Titanium dioxide (TiO2) nanoparticles (NPs) have made unbelievable progress in the field of nanotechnology and biomedical research. The proper toxicological assessment of TiO2 NPs and the reduction of its cytotoxicity need to be addressed. Fe doping in TiO2 has been investigated to reduce the toxic effects of TiO2 NPs. Fe doped TiO2 powder samples were synthesized by sol-gel methods. The prepared samples were characterized by x-ray diffractometer (XRD), transmission electron microscope (TEM), and Raman spectroscopy to study their structure, morphology, and molecular conformation. XRD results revealed the coexistence of anatase (A) and rutile (R) phases of TiO2. The A-R transformation was observed with an increase in Fe doping along with the formation of α-Fe2O3 phase. TEM showed changes in morphology from spherical nanoparticles to elongated rod-shaped nanostructures with increasing Fe content. Shape variation of TiO2 nanoparticles after incorporation of Fe is a key reason behind the toxicity reduction. The authors observed that the toxicity of TiO2 nanoparticles was rescued upon Fe incorporation. The effect of NPs on the mitochondrial membrane potential (MMP) was assessed using flow cytometry. The MMP (%) decreased in TiO2 treated cells and increased by 1% Fe doped TiO2 NPs treated cells. Confocal imaging revealed the presence of functional mitochondria upon the exposure of Fe doped TiO2 NPs. The goal of the present study was to decrease the toxic effects induced by TiO2 NPs on mitochondrial potential and its prevention by Fe doping.

Awakening the "guardian of genome": reactivation of mutant p53.

The role of tumor suppressor protein p53 is undeniable in the suppression of cancer upon oncogenic stress. It induces diverse conditions such as cell-cycle arrest, cell death, and senescence to protect the cell from carcinogenesis. The rate of mutations in p53 gene nearly accounts for 50% of the human cancers. Upon mutations, the conformation gets altered and becomes non-native. Mutant p53 displays long half-life and accumulates in the nucleus and interacts with oncoproteins to promote carcinogenesis and these interactions present a formidable challenge for clinicians in therapy of the disease. Variety of approaches have been developed, through which native-like function of p53 can be restored, such as restoration of the native-like structure of p53, activating the p53 family members, etc. Modern scientific techniques have led to the discovery of a variety of molecules to reactivate mutant p53 and restore its transcriptional activity. These compounds include small molecules, various peptides, and phytochemicals. In this review article, we comprehensively discuss these molecules to reactivate mutant p53 to restore the normal function with a particular focus on molecular mechanisms.

Sequence based prediction of enhancer regions from DNA random walk.

Regulatory elements play a critical role in development process of eukaryotic organisms by controlling the spatio-temporal pattern of gene expression. Enhancer is one of these elements which contributes to the regulation of gene expression through chromatin loop or eRNA expression. Experimental identification of a novel enhancer is a costly exercise, due to which there is an interest in computational approaches to predict enhancer regions in a genome. Existing computational approaches to achieve this goal have primarily been based on training of high-throughput data such as transcription factor binding sites (TFBS), DNA methylation, and histone modification marks etc. On the other hand, purely sequence based approaches to predict enhancer regions are promising as they are not biased by the complexity or context specificity of such datasets. In sequence based approaches, machine learning models are either directly trained on sequences or sequence features, to classify sequences as enhancers or non-enhancers. In this paper, we derived statistical and nonlinear dynamic features along with k-mer features from experimentally validated sequences taken from Vista Enhancer Browser through random walk model and applied different machine learning based methods to predict whether an input test sequence is enhancer or not. Experimental results demonstrate the success of proposed model based on Ensemble method with area under curve (AUC) 0.86, 0.89, and 0.87 in B cells, T cells, and Natural killer cells for histone marks dataset.

High expression of FBP17 in invasive breast cancer cells promotes invadopodia formation.

Metastatic spread of the cancer is usually the consequence of the activation of signaling pathways that generate cell motility and tissue invasion. Metastasis involves the reorganization of cytoskeleton and cell shape for the swift movement of the cells through extracellular matrix. Previously, we have described the invasive and metastatic role played by one of the members (Toca-1) of CIP4 subfamily of F-BAR proteins. In the present study, we address the role of another member (FBP17) of same family in the invasion breast cancer cells. Here, we report that the formin-binding protein 17 (FBP17) is highly expressed at both mRNA and protein levels in breast cancer cells. The study showed the association of FBP17 with cytoskeletal actin regulatory proteins like dynamin and cortactin. To determine its role in extracellular matrix (ECM) degradation, we achieved stable knockdown of FBP17 in MDA-MB-231 cells. FBP17 knockdown cells showed a defect and were found to be compromised in the degradation of ECM indicating the role of FBP17 in the invasion of breast cancer cells. Our results suggest that FBP17 is highly expressed in breast cancer cells and facilitates the invasion of breast cancer cells.