Expedited Proton Relay in Enzyme-Inspired Cobaloximes Facilitates Organic Transformations.

Developing a water-soluble, oxygen-tolerant, and acid-stable synthetic H2 production catalyst is vital for renewable energy infrastructure. To access such an effective catalyst, we strategically incorporated enzyme-inspired, multicomponent outer coordination sphere elements around the cobaloxime (Cl-Co-X) core with suitable axial coordination (X). Our cobaloximes with axial imidazole or L-histidine coordination in photocatalytic HAT including the construction of anilines via a non-canonical cross-coupling approach is found superior compared to commonly used cobaloxime catalysts. The reversible Co(II)/Co(I) process is influenced by the axial N ligand's nature. Imidazole/ L-histidine with a higher pKa promptly produces H2 upon irradiation, leading to the improved reactivity compared to previously employed axial (di)chloride or pyridine analogue.

Expanding the Horizon of Bio-Inspired Catalyst Design with Tactical Incorporation of Drug Molecules.

The development of potent H2 production catalysts is a key aspect in our journey toward the establishment of a sustainable carbon-neutral power infrastructure. Hydrogenase enzymes provide the blueprint for designing efficient catalysts by the rational combination of central metal core and protein scaffold-based outer coordination sphere (OCS). Traditionally, a biomimetic catalyst is crafted by including natural amino acids as OCS features around a synthetic metal motif to functionally imitate the metalloenzyme activity. Here, we have pursued an unconventional approach and implanted two distinct drug molecules (isoniazid and nicotine hydrazide) at the axial position of a cobalt core to create a new genre of synthetic catalysts. The resultant cobalt complexes are active for both electrocatalytic and photocatalytic H2 production in near-neutral water, where they significantly enhance the catalytic performance of the unfunctionalized parent cobalt complex. The drug molecules showcased a dual effect as they influence the catalytic HER by improving the surrounding proton relay along and exerting subtle electronic effects. The isoniazid-ligated catalyst C1 outperformed the nicotine hydrazide-bound complex C2, as it produced H2 from water (pH 6.0) at a rate of 3960 s-1 while exhibiting Faradaic efficiency of about 90 %. This strategy opens up newer avenues of bio-inspired catalyst design beyond amino acid-based OCS features.

Improved detection accuracy of red lesions in retinal fundus images with superlearning approach.

BACKGROUND: Diabetic Retinopathy (DR) is a serious consequence of diabetes that can result to permanent vision loss for a person. Diabetes-related vision impairment can be significantly avoided with timely screening and treatment in its initial phase. The earliest and the most noticeable indications on the surface of the retina are micro-aneurysm and haemorrhage, which appear as dark patches. Therefore, the automatic detection of retinopathy begins with the identification of all these dark lesions. METHOD: In our study, we have developed a clinical knowledge based segmentation built on Early Treatment DR Study (ETDRS). ETDRS is a gold standard for identifying all red lesions using adaptive-thresholding approach followed by different pre-processing steps. The lesions are classified using super-learning approach to improve multi-class detection accuracy. Ensemble based super-learning approach finds optimal weights of base learners by minimizing the cross validated risk-function and it pledges the improved performance compared to base-learners predictions. For multi-class classification, a well informative feature-set based on colour, intensity, shape, size and texture, is developed. In this work, we have handled the data imbalance problem and compared the final accuracy with different synthetic data creation ratios. RESULT: The suggested approach uses publicly available resources to perform quantitative assessments at lesions-level. The overall accuracy of red lesion segregation is 93.5%, which has increased to 97.88% when data imbalance problem is taken care-off. CONCLUSION: The results of our system have achieved competitive performance compared with other modern approaches and handling of data imbalance further increases the performance of it.

Improving the synthetic H2 production catalyst design strategy with the neurotransmitter dopamine.

The strategic incorporation of the neurotransmitter dopamine around a cobaloxime core resulted in excellent electrocatalytic (rate 8400 s-1) and photocatalytic H2 production under neutral aqueous conditions. The influence of the synthetic outer coordination sphere features continues even with a phenylene-diimino-dioxime motif-coordinated cobalt core.

Peripheral nucleic bases boost H2 production by synthetic molecular catalysts in acidic water.

The strategic inclusion of nucleic bases adenine, cytosine, and thymine, in the form of outer coordination sphere, positively impacts the electro- and photocatalytic H2 production by cobaloxime cores. These cobaloxime derivatives showcased their optimal H2 production in acidic media due to specific protonation of adenine and cytosine below pH 5.0.

Energy-efficient CO2/CO interconversion by homogeneous copper-based molecular catalysts.

Facile conversion of CO2 to commercially viable carbon feedstocks offer a unique way to adopt a net-zero carbon scenario. Synthetic CO2-reducing catalysts have rarely exhibited energy-efficient and selective CO2 conversion. Here, the carbon monoxide dehydrogenase (CODH) enzyme blueprint is imitated by a molecular copper complex coordinated by redox-active ligands. This strategy has unveiled one of the rarest examples of synthetic molecular complex-driven reversible CO2 reduction/CO oxidation catalysis under regulated conditions, a hallmark of natural enzymes. The inclusion of a proton-exchanging amine groups in the periphery of the copper complex provides the leeway to modulate the biases of catalysts toward CO2 reduction and CO oxidation in organic and aqueous media. The detailed spectroelectrochemical analysis confirms the synchronous participation of copper and redox-active ligands along with the peripheral amines during this energy-efficient CO2 reduction/CO oxidation. This finding can be vital in abating the carbon footprint-free in multiple industrial processes.

Multidrug-resistant Staphylococcus pettenkoferi isolated from cat in India.

BACKGROUND AND AIM: Coagulase-negative staphylococci (CoNS) are considered to be one of the emerging pathogens in human and animals in recent times. Staphylococcus pettenkoferi, a novel pathogen under CoNS, is discovered in 2002 in humans with multiple clinical manifestations in various patients. To date, the pathogens have not yet been reported from any animals. The present study reported the first ever isolation, identification, and characterization of multidrug-resistant S. pettenkoferi from a cat with peritonitis in India. MATERIALS AND METHODS: Peritoneal fluid was collected aseptically from 3 years old cat processed for bacteriological culture by standard techniques. Isolates were confirmed by BD Phoenix™ automated bacterial identification system and were subjected to plate and tube coagulase tests. All the isolates were tested for antimicrobial sensitivity profile by disc diffusion assay, extended-spectrum ß-lactamase production by double disc diffusion assay, in vitro biofilm production ability by microtiter plate assay, and detection of virulence genes and mecA gene by polymerase chain reaction assay. RESULTS: A total of five clonally expanded isolates of S. pettenkoferi were isolated from peritoneal fluid of the affected cat. All the isolates were resistant against 36 antimicrobial agents and were also methicillin-resistant staphylococci. Phenotypically, all the isolates were negative for biofilm production but were carrying multiple biofilm-producing genes (icaA, IS257, nuc, and mecA). CONCLUSION: Although S. pettenkoferi was previously reported once from animal (cat) environment, this is probably the first ever report of isolation of the organism directly from any animals. This is also probably the first report from any species in India.

Iron(ii) complexes of 2-mercaptopyridine as rubredoxin site analogues.

The synthesis, characterization and reactivity studies of iron(ii) complexes [FeII(PySH)4](OTf)2, 1-(OTf)2, [FeII(PySH)4](ClO)4, 1-(ClO4)2, and [FeII(PyS)2]n, (2), of a 2-mercaptopyridine (PySH) ligand are discussed. The X-ray crystal structures of both 1-(OTf)2 and 1-(ClO4)2 reveal a distorted tetrahedral geometry at the iron(ii) center with identical constituents. All the pyridine nitrogen atoms are protonated and thiolate ions are coordinated to the iron(ii) center. The structure and function of complex 1-(OTf)2 or 1-(ClO4)2 resembles the active site of rubredoxin. Complex 2 has octahedral geometry at the iron(ii) center forming a 1-D coordination polymer. Complex 1-(OTf)2 exhibits a high positive redox potential (E1/2 = 0.23 V vs. Ag/AgCl) which reduces to -0.12 V in the presence of triethylamine under an inert atmosphere. This change of the redox potential is highly reversible in the presence of a weak acid such as p-toluenesulfonic acid, pTsOH. DFT studies show that the complex cation [FeII(PySH)4]2+ upon treatment with a base converts to its anionic congener, [FeII(PyS)4]2-, via the deprotonation of the pyridinium moiety. The iron(ii) complexes readily react with molecular oxygen to yield the corresponding iron(iii) complex, which rapidly decays to form pyridine disulphide (Py2S2) and an iron(ii) complex.

A multi-fold string kernel for sequence classification.

A novel framework is proposed to classify biological sequences using a kernel. It considers the topological information along with the primary structural information. The widely used string kernel for sequence classification does not take into account the structural information which might be available for biological sequences. The proposed kernels incorporate the additional structural information and thus make the features more informative.

Cancer classification from gene expression data by NPPC ensemble.

The most important application of microarray in gene expression analysis is to classify the unknown tissue samples according to their gene expression levels with the help of known sample expression levels. In this paper, we present a nonparallel plane proximal classifier (NPPC) ensemble that ensures high classification accuracy of test samples in a computer-aided diagnosis (CAD) framework than that of a single NPPC model. For each data set only, a few genes are selected by using a mutual information criterion. Then a genetic algorithm-based simultaneous feature and model selection scheme is used to train a number of NPPC expert models in multiple subspaces by maximizing cross-validation accuracy. The members of the ensemble are selected by the performance of the trained models on a validation set. Besides the usual majority voting method, we have introduced minimum average proximity-based decision combiner for NPPC ensemble. The effectiveness of the NPPC ensemble and the proposed new approach of combining decisions for cancer diagnosis are studied and compared with support vector machine (SVM) classifier in a similar framework. Experimental results on cancer data sets show that the NPPC ensemble offers comparable testing accuracy to that of SVM ensemble with reduced training time on average.

Discriminant analysis for fast multiclass data classification through regularized kernel function approximation.

In this brief we have proposed the multiclass data classification by computationally inexpensive discriminant analysis through vector-valued regularized kernel function approximation (VVRKFA). VVRKFA being an extension of fast regularized kernel function approximation (FRKFA), provides the vector-valued response at single step. The VVRKFA finds a linear operator and a bias vector by using a reduced kernel that maps a pattern from feature space into the low dimensional label space. The classification of patterns is carried out in this low dimensional label subspace. A test pattern is classified depending on its proximity to class centroids. The effectiveness of the proposed method is experimentally verified and compared with multiclass support vector machine (SVM) on several benchmark data sets as well as on gene microarray data for multi-category cancer classification. The results indicate the significant improvement in both training and testing time compared to that of multiclass SVM with comparable testing accuracy principally in large data sets. Experiments in this brief also serve as comparison of performance of VVRKFA with stratified random sampling and sub-sampling.