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Thermal springs have been the most resourceful ecological niches to understand the intricacies of the microbial community structure building. In the present study, the microbial community structure...

Hydrocarbons originated from automobile oils are significant polluting agents of the soil. Lipase-mediated catalytic degradation of hydrocarbons from this used automobile oil is attempted in the present approach. Lipase was produced from peanut oil industry waste biomass using thermophilic alkaline-tolerant Pseudomonas sp. S1 obtained from sediment of Taptapani hot spring, Odisha. The experiments were designed as per central composite design, which is response surface methodology, to study the effect of the parameters and their interaction on the activity. A quadratic regression was found to be best with the experimental data, R2 = 0.899741 and the maximum lipase production of 51.3 IU/g was predicted from peanut oil seed waste biomass at an optimum pH of 8.31, temperature of 50.58 °C, moisture content of 50.8%, and biosurfactant content of 1.051 mg. The significant interaction of pH and moisture content with biosurfactant during lipase production was observed through the contour plots and 3D surface responses. Pseudomonas sp. S1 could degrade 92.6% of the used automobile oil after incubation for 24 h under optimum pH of 8.31 and temperature of 50.58 °C. Degradation of petroleum hydrocarbons by this thermophilic isolate could be effective due to the ability of lipase to utilize the used automobile oils as sole source of carbon and energy.

The emergence of colistin-carbapenem-resistant Klebsiella pneumoniae (CCR-Kp) in bloodstream infection results in high mortality, and virulence factor contributes further to the difficulty of treatment. A total of 158 carbapenem-resistant K. pneumoniae (CRKP) isolates causing bloodstream infection were collected from three Indian tertiary care hospitals during the 9-month study period, of which 27 isolates exhibited resistance to both colistin and carbapenem antibiotics. In this study, all the strains were characterized for antimicrobial resistance, virulence factors and capsular serotypes that facilitate the development of colistin and carbapenem-resistant K.pneumoniae (CCR-Kp) in bloodstream infection. Fourteen isolates displayed extremely drug resistance (XDR), susceptible only to tigecycline, and the remaining 13 isolates displayed multidrug resistance (MDR). The gene prevalence analysis for CCR-Kp isolates showed the predominance of bla(KPC) (81.48%) followed by bla(NDM) (62.96%), bla(VIM) (37.03%) and bla(IMP) (18.51%) genes. The distribution of virulence genes was found to be fimH (81.48%), wabG (59.25%), mrkD (55.56%), entB (48.15%), irp1 (33.33%), and rmpA (18.52%). The capsular serotypes K1, K2, K5 and K54 have been identified in 16 isolates. The absence of plasmid-mediated colistin resistance (mcr) genes implies the involvement of other mechanisms. The ERIC and (GTG)(5) molecular typing methods detected 18 and 22 distinct clustering patterns among the CCR-Kp isolates, respectively. A strong correlation between ERIC and (GTG)5 genotyping method was established with antimicrobial resistance patterns and virulence determinants at P

Lipase based formulations has been a rising interest to laundry detergent industry for their ecofriendly property over phosphate-based counterparts and compatibility with chemical detergents ingredients. A thermo-stable Anoxybacillus sp. ARS-1 isolated from Taptapani Hotspring, India was characterized for optimum lipase production employing statistical model central composite design (CCD) under four independent variables (temperature, pH, % moisture and bio-surfactant) by solid substrate fermentation (SSF) using mustard cake. The output was utilized to find the effect of parameters and their interaction employing response surface methodology (RSM). A quadratic regression with R2¼0.955 established the model to be statically best fitting and a predicted highest lipase production of 29.4IU/g at an optimum temperature of 57.5oC, pH 8.31, moisture 50% and 1.2mg of bio-surfactant. Experimental production of 30.3IU/g lipase at above conditions validated the fitness of model. Anoxybacillus sp. ARS-1 produced lipase was found to resist almost all chemical detergents as well as common laundry detergent, proving it to be a prospective additive for incorporation.

This paper aims to develop and evaluate two different webcam-based gaze-controlled interfaces for users with severe speech and motor impairment (SSMI). We configured two webcam-based gaze trackers using open-source software (Python and JavaScript) and developed cursor control algorithm using the gaze tracker. We designed a quiz application to evaluate the webcam-based gaze trackers for both users with SSMI and their able-bodied counterparts. We also collected data using a commercial infrared-based eye gaze tracker. We noted that users with SSMI and able- bodied users could use the webcam-based gaze-controlled interface. It was found that for users with SSMI, speed of interaction was significantly faster for a low-cost infrared-based commercial gaze. Results from this study can be used to develop as well as select low-cost eye gaze trackers for users with SSMI. This might be the first study to evaluate webcam-based gaze trackers in a gaze-controlled interface for users with different range of abilities

Extreme environments are the main source of industrially suitable biocatalysts. The non-cultivable approach of searching enzymes is known to provide ample scope to accomplish novelty for their industrial applications. Lip479 clone out of seven lipase-producing clones obtained from Taptapani hot spring was found to be optimally active at pH 8.0 and temperature 65 °C. The recombinant Lip479 was highly stable in organic solvents, methanol, DMF, DMSO, acetone, and dichloromethane. Lip479 lipase activity was enhanced in the presence of K+, Mn2+, Na+, Zn2+, and Ca2+ except for Fe3+. The ability of Lip479 lipase to act on long carbon chain of 4-nitrophenyl myristate suggests it might be a true lipase. Lip479 clone was found to have ORF of 1251 bp encoding 416 amino acid residues of 42.57 KDa size (theoretically calculated). The presence of conserved motif Ala-His-Ser-Gln-Gly and Zn2+-binding consensus sequence (GAAHAAKH) of the clone assigns the protein to lipase family 1.5. Phylogenetic lineage of the protein sequence of Lip479 was traced to family 1.5 as it was clubbed up with those of reported lipases of the same family. The above biochemical features indicated that Lip479 lipase can be a potential biocatalyst for its use in various industries.

Arsenic is a gray-appearing metalloid which occurs naturally and is the 20th most prolific element in the earth’s crust. It is an integral part of more than 200 minerals. These are mostly ores containing sulfides, along with copper, nickel, lead, and other metals. In the environment, arsenic and its compounds are very mobile. Although in its organic form arsenic is nontoxic, it is highly toxic in its inorganic form (arsenite, a free form of arsenic) with arsine gas being the most fatal. The World Health Organization recommends a concentration below 20 mg/l for an individual to be considered free of arsenic poisoning. Accumulation of arsenic in the body beyond this level could adversely affect human health. An individual suffering from chronic arsenic poisoning via contaminated water could suffer from severe skin-related ailments like melanosis (pigmentation of the skin), keratosis (associated with the formation of rough, dry, and popular skin lesions), and leucomelanosis (also known as spotted melanosis) ultimately leading to arsenicosis in the long term. Other than that arsenic poisoning also may lead to other manifestations like neurological disorders, diabetes mellitus, high blood pressure, obstetric problems, disorders of the respiratory system, and cancer in the lung, skin, and bladder. The Indian subcontinent is very rich in arsenic, and countries like India and Bangladesh are a disaster waiting to happen. West Bengal, India, is a state severely affected by arsenic-contaminated water, and a case study showed an astounding 16 sites from one single village with very high concentrations of arsenic. As such, it is the need of the hour for governments to be ready with an immediate action plan to tackle such large-scale disasters. Existing solutions to this problem include phytoremediation via hyperaccumulation with plants like Pteris vittata and grasses like A. delicatula and use of phosphate-based fertilizers. However, a long-term use of phosphate-based fertilizers may ultimately lead to an algal bloom in water bodies, and phytoremediation is a time-consuming process. Planktons, however, have the potential to be a game changer in tackling arsenic-contaminated water bodies by virtue of accumulation and bioremediation. Spirulina platensis, a typical plankton, produces an enzyme called arsenite S-adenosylmethionine methyltransferase which has the ability to methylate arsenic making it nontoxic. This enzyme confers Spirulina platensis the unique ability to convert the toxic trivalent arsenic to its nontoxic pentavalent form. Spirulina platensis produces this enzyme by the virtue of arsenite S-adenosylmethionine methyltransferase (SpArsM) gene.

In the oil and gas industry, there is a growing demand for application of big data analytics and artificial intelligence (AI) technologies to optimize operations and reduce cost. In this study, we work on the productivity prediction, which is an important and challenging task for operators. Unlike previous studies where full field or single well analysis was conducted, we focus on more active operation units, drilling space units. Moreover, significant information is extracted from geology reports which are saved in scanned PDF files and well logs. By using the extracted information, long short-term memory (LSTM) model which could take the special-temporal changes of DSUs into consideration is employed to predict the DSUs' productivity. After rigorous validation, it is found that the accuracy of LSTM model could reach to more than 60%, which is 10% higher than a multilayer perceptron (MLP) model proposed in a previous research.

Manufacturing industries collect massive amounts of multivariate measurement through automated inspection processes. Noisy measurements and high-dimensional, irrelevant features make it difficult to identify useful patterns in the data. Principal component analysis provides linear summaries of datasets with fewer latent variables. Kernel Principal Component Analysis (KPCA), however, identifies nonlinear patterns. One challenge in KPCA is to inverse map the denoised signal from a high-dimensional feature space into its preimage in input space to visualize the nonlinear variation sources. However, such an inverse map is not always defined. This article provides a new meta-method applicable to any KPCA algorithm to approximate the preimage. It improves upon previous work where a strong assumption was the availability of noise-free training data. This is problematic for applications such as manufacturing variation analysis. To attenuate noise in kernel subspace estimation the final preimage is estimated as th...

Image denoising is an important technique of practical significance which serves as a preliminary step for other analyses like image feature extraction, image classification etc. Two novel methods for denoising images are proposed which deal with the case when there is no noise-free training data. The basic method consists of several phases: the first phase involves preprocessing the given noisy data matrix to obtain a good approximation matrix; the second phase involves implementing kernel principal component analysis (KPCA) on the approximation matrix obtained from the first phase. KPCA is one of the useful non-linear techniques applied to image denoising. However, an important problem faced in KPCA is estimating the denoised pre-image. Consequently, we generate a pre-image by solving a regularized regression problem. The second method is an ensemble version of the basic method that provides robustness to noisy instances. Some of the attractive properties of the proposed methods include numerical stability and ease of implementation. Also our methods are based on linear algebra and avoid any nonlinear optimization. Our methods are demonstrated on high-noise cases (for both Gaussian noise and “salt and pepper” noise) for the USPS digits dataset, and they perform better than existing alternatives both in terms of low mean square error and better visual quality of the reconstructed pre-images.