Generalised non-negative matrix factorisation for air pollution source apportionment.

Source Apportionment (SA) techniques are widely used for identifying key sources of air pollution, thereby providing critical inputs for policy measures. Positive Matrix Factorisation (PMF) (Paatero and Tapper, 1994) is a widely used SA technique. PMF uses the speciated concentration data (X) collected over several days and factorises it into source contribution (G) and source profile (F) matrices, albeit under positivity constraint. Towards this end, it involves solving an optimisation problem where the elements of X are weighted by the inverse of the standard deviations of the corresponding errors introduced during the sampling and chemical analysis process. Thus, PMF implicitly assumes that the errors in different elements of the X matrix are uncorrelated. This assumption may not hold since the sampling, and chemical analysis steps deployed in any data-collection campaign will inevitably lead to correlated errors. While there are other existing Non-Negative Matrix Factorisation (NMF) methods in literature that can be potentially used for SA, these also make various restrictive assumptions about the error covariance structure. In this work, we propose a new method called Generalised Non-Negative Matrix Factorisation (GNMF) to fill this gap. In particular, the proposed method is able to incorporate any error covariance matrix without making any restrictive assumptions on its structure. Towards this end, we integrate the full error covariance matrix in the objective function to be minimised to obtain F and G matrices. We derive the corresponding update rules for obtaining these matrices iteratively. To ensure non-negativity, we extend the multiplicative and projected gradient-based ideas available in NMF literature to the proposed GNMF approach. The proposed method subsumes various NMF methods available in literature as special cases. The utility of the proposed approach is demonstrated by comparing its performance with other methods on an SA problem using a dataset derived from field measurements.

Fournier's Gangrene Due to Salmonella Typhimurium in a Healthy Male: A Rare Aberrant Infection.

INTRODUCTION: Human Salmonella infections have been classically distinguished into diseases caused by typhoidal and non-typhoidal salmonella (NTS). Typhiodal salmonella includes S. enterica serovars Typhi and Paratyphi that cause the systemic disease but are restricted to human infections, while NTS consists mainly of other serovars that predominantly cause self-limiting gastroenteritis in humans. Localisation of foci with persisting infection occurs due to dissemination of the bacteria throughout the body and can cause a variety of rare clinical syndromes at aberrant sites. Fournier's gangrene, a rapidly progressive, often fatal, necrotizing fasciitis of the external genitalia and perineum due to Salmonella typhimurium, is a rare manifestation and has never been reported. CASE: A 22-year-old male, apparently healthy patient with no relevant past medical history, was presented to surgical emergency with chief complaints of swelling of bilateral scrotal area. Infective etiology was considered and a diagnosis of Fournier's gangrene was made. Pure growth of Salmonella typhimurium was obtained after repeated subculture and was identified biochemically and on serotyping, as Salmonella enterica, Serotype typhimurium using specific antisera. CONCLUSION: In our case report, we describe a case of Fournier's gangrene due to Salmonella Typhimurium in an otherwise healthy male to highlight the unusual presentation of Non typhoidal salmonellae at an aberrant site. We also emphasize the importance of using selective media like Selenite F broth for isolation of Salmonella typhimurium from a pus sample.

Weight-of-evidence approach to identify regionally representative sites for air-quality monitoring network: Satellite data-based analysis.

The methodology discussed in Lekinwala et al., 2020, hereinafter referred to as the 'parent article', is used to setup a nation-wide network for background PM2.5 measurement at strategic locations, optimally placing sites to obtain maximum regionally representative PM2.5 concentrations with minimum number of sites. Traditionally, in-situ PM2.5 measurements are obtained for several potential sites and compared to identify the most regionally representative sites [4], Wongphatarakul et al., 1998) at the location. The 'parent article' proposes the use of satellite-derived proxy for aerosol (Aerosol Optical Depth, AOD) data in the absence of in-situ PM2.5 measurements. This article focuses on the details about satellite-data processing which forms part of the methodology discussed in the 'parent article'. Following are some relevant aspects:•High resolution AOD is retrieved from Moderate Resolution Imaging Spectroradiometer (MODIS) instruments aboard NASA's Aqua and Terra satellite using Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm. The data is stored as grids of size 1200  ×  1200 and a total of seven such grids cover the Indian land mass. These grids were merged, regridded and multiplied by conversion factors from GEOS-Chem Chemical Transport Model to obtain PM2.5 values. Standard set of tools like CDO and NCL are used to manipulate the satellite-data (*.nc files).•The PM2.5 values are subjected to various statistical analysis using metrics like coefficient of divergence (CoD), Pearson correlation coefficient (PCC) and mutual information (MI).•Computations for CoD, MI are performed using Python codes developed in-house while a function in NumPy module of Python was used for PCC calculations.

Aerosols cause intraseasonal short-term suppression of Indian monsoon rainfall.

Aerosol abundance over South Asia during the summer monsoon season, includes dust and sea-salt, as well as, anthropogenic pollution particles. Using observations during 2000-2009, here we uncover repeated short-term rainfall suppression caused by coincident aerosols, acting through atmospheric stabilization, reduction in convection and increased moisture divergence, leading to the aggravation of monsoon break conditions. In high aerosol-low rainfall regions extending across India, both in deficient and normal monsoon years, enhancements in aerosols levels, estimated as aerosol optical depth and absorbing aerosol index, acted to suppress daily rainfall anomaly, several times in a season, with lags of a few days. A higher frequency of prolonged rainfall breaks, longer than seven days, occurred in these regions. Previous studies point to monsoon rainfall weakening linked to an asymmetric inter-hemispheric energy balance change attributed to aerosols, and short-term rainfall enhancement from radiative effects of aerosols. In contrast, this study uncovers intraseasonal short-term rainfall suppression, from coincident aerosol forcing over the monsoon region, leading to aggravation of monsoon break spells. Prolonged and intense breaks in the monsoon in India are associated with rainfall deficits, which have been linked to reduced food grain production in the latter half of the twentieth century.

Metabolic modeling for multi-objective optimization of ethanol production in a Synechocystis mutant.

Cyanobacteria have potential to produce drop-in bio-fuels such as ethanol via photoautotrophic metabolism. Although model cyanobacterial strains have been engineered to produce such products, systematic metabolic engineering studies to identify optimal strains for the same have not been performed. In this work, we identify optimal ethanol producing mutants corresponding to appropriate gene deletions that result in a suitable redirection in the carbon flux. In particular, we systematically simulate exhaustive single and double gene deletions considering a genome scale metabolic model of a mutant strain of the unicellular cyanobacterium Synechocystis species strain PCC 6803. Various optimization based metabolic modeling techniques, such as flux balance analysis (FBA), method of minimization of metabolic adjustment (MOMA) and regulatory on/off minimization (ROOM) were used for this analysis. For single gene deletion MOMA simulations, the Pareto front with biomass and ethanol fluxes as the two objectives to be maximized was obtained and analyzed. Points on the Pareto front represent maximal utilization of resources constrained by substrate uptake thereby representing an optimal trade-off between the two fluxes. Pareto analysis was also performed for double gene deletion MOMA and single and double gene deletion ROOM simulations. Based on these analyses, two mutants, with combined gene deletions in ethanol and purine metabolism pathways, were identified as promising candidates for ethanol production. The relevant genes were adk, pta and ackA. An ethanol productivity of approximately 0.15 mmol/(gDW h) was predicted for these mutants which appears to be reasonable based on experimentally reported values in literature for other strains.

Metabolic flexibility of D-ribose producer strain of Bacillus pumilus under environmental perturbations.

The metabolic reaction rate vector is a bridge that links gene and protein expression alterations to the phenotypic endpoint. We present a simple approach for the estimation of flux distribution at key branch points in the metabolic network by using substrate uptake, metabolite secretion rate, and biomass growth rate for transketolase (tkt) deficient Bacillus pumilus ATCC 21951. We find that the glucose-6-phosphate (G6P) and pseudo catabolic/anabolic branch points are flexible in the D: -ribose-producing tkt deficient strain of B. pumilus. The normalized flux through the pentose phosphate pathway (PPP) varied from 1.5 to 86 % under different growth conditions, thereby enabling substantial extracellular accumulation of D: -ribose under certain conditions. Interestingly, the flux through PPP was affected by the extracellular phosphate concentration and dissolved oxygen concentration. This metabolic flexibility may have been the underlying reason for this strain being selected from thousands of others in a screening for D: -ribose producers conducted in the 1970s.

Macrocephaly-capillary malformation syndrome: three new cases.

Overgrowth syndromes can be associated with asymmetry, obesity and various vascular malformations. Macrocephaly-Capillary Malformation (M-CM) is a more recently defined overgrowth syndrome characterized by cutaneous capillary malformation occurring in association with macrocephaly with tendency to progressive enlargement, abnormalities of somatic growth with body asymmetry including brain asymmetry, developmental delay, typical face with full cheeks, nevus flammeus of the nose and/or philtrum and upper lip, joint laxity, thickened subcutaneous tissue and 2/3 syndactyly of the toes. We evaluated three patients who demonstrated characteristic features of the disorder. Patients seen in the Genetic clinic of a tertiary care center were subjects of the analysis. We present three cases of overgrowth syndrome with common features of macrocephaly, capillary malformation, dysmorphic face and abnormal neurocognitive profile. These features are consistent with the newly defined M-CM syndrome. This condition must be differentiated from other overgrowth syndromes for appropriate surveillance for known complications and genetic counseling. We discuss the diagnostic criteria for the disorder and also recommend to include typical face with full cheeks, nevus flammeus of the nose and/or philtrum and upper lip, considering it as minor criterion on the basis of findings in present cases. One of the cases had bluish white iris which has not been described earlier.

Multi-objective optimization of glycopeptide antibiotic production in batch and fed batch processes.

Fermentation optimization involves potentially conflicting multiple objectives such as product concentration and production media cost. Simultaneous optimization of these objectives would result in a multiobjective optimization problem, which is characterized by a set of multiple solutions, knows as pareto optimal solutions. These solutions gives flexibility in evaluating the trade-offs and selecting the most suitable operating policy. Here, ε-constraint approach was used to generate the pareto solutions for two objectives: product concentration and product per unit cost of media, for batch and fed batch operations using process model for Amycolatopsis balhimycina, a glycopeptide antibiotic producer. This resulted in a set of several pareto optimal solutions with the two objectives ranging from (0.75 g l(-1), 3.97 g $(-1)) to (0.44 g l(-1), 5.19 g $(-1)) for batch and from (1.5 g l(-1), 5.46 g $(-1)) to (1.1 g l(-1), 6.34 g $(-1)) for fed batch operations. One pareto solution each for batch and for fed batch mode was experimentally validated.

Substrate uptake, phosphorus repression, and effect of seed culture on glycopeptide antibiotic production: process model development and experimental validation.

Actinomycetes, the soil borne bacteria which exhibit filamentous growth, are known for their ability to produce a variety of secondary metabolites including antibiotics. Industrial scale production of such antibiotics is typically carried out in a multi-substrate medium where the product formation may experience catabolite repression by one or more of the substrates. Availability of reliable process models is a key bottleneck in optimization of such processes. Here we present a structured kinetic model to describe the growth, substrate uptake and product formation for the glycopeptide antibiotic producer strain Amycolatopsis balhimycina DSM5908. The model is based on the premise that the organism is an optimal strategist and that the various metabolic pathways are regulated via key rate limiting enzymes. Further, the model accounts for substrate inhibition and catabolite repression. The model is also able to predict key phenomena such as simultaneous uptake of glucose and glycerol but with different specific uptake rates, and inhibition of glycopeptide production by high intracellular phosphate levels. The model is successfully applied to both production and seed medium with varying compositions and hence has good predictive ability over a variety of operating conditions. The model parameters are estimated via a well-designed experimental plan. Adequacy of the proposed model was established via checking the model sensitivity to its parameters and confidence interval calculations. The model may have applications in optimizing seed transfer, medium composition, and feeding strategy for maximizing production.