Xylanase Production by Isolated Fungal Strain, Aspergillus fumigatus RSP-8 (MTCC 12039): Impact of Agro-industrial Material as Substrate

In the present investigation, the imperative role of agro-industrial biomass for improved xylanase production was evaluated using isolated fungal strain. This isolate was identified as Aspergillus fumigatus RSP-8 (MTCC 12039) based on morphological and 18S rRNA ribotyping and the organism was deposited in MTCC, IMTECH Chandigarh with accession number 12039. The isolated fungal strain is mesophilic in nature and produced maximum xylanase at 30 °C, at pH 7 and agitation speed of 150 rpm. Xylanase complex production titers differed with the nature and complexity of carbon source and other physiological growth parameters including aeration, growth temperature, physiological medium pH, initial inoculum levels, etc. Highest xylanase titers (73 U/mL) noticed with hemicellulose isolated from sorghum straw and least with ground nut cake as carbon source among tested agro materials such as rice bran, green gram husk, sorghum straw, groundnut cake and wheat bran. A variation of three fold enzyme titers was observed with different tested carbon sources. Supplementation of glucose as carbon source did not produce any xylanase with this fungal strain revealing the xylanase in this isolate is induced by the carbon source. Variation of hemicellulose concentration as carbon source during the fermentation altered the production xylanase titers. The study suggested that, in xylanase production by A. fumigatus RSP-8, one of the major limiting factors is substrate chemical complexity

Quantification of Dhurrin in Different Types of Sorghum Forages by Near-Infrared Reflectance Spectroscopy

Hydrogen cyanide (HCN) is a toxic chemical that can potentially cause mild to severe reactions in animals upon feeding forage sorghum. Developing technologies to monitor the level of HCN in the growing crop could prevent livestock poisoning. In this study ncar-infrared spectroscopy (NIRS) calibration to estimate HCN in forage sorghum is developed. The full spectral NIRS range (1100-2498 nm) was used as well as specific spectral ranges within the full spectral range, i.e. , visible (400-750 nm), short wave (800-1100 nm) and near - infrared (NIR) (1100-2498 nm). Using the full spectrum approach and the modified partial least-squares (MPLS), the calibration produced a coefficient of determination (R2) = 0.869 and standard error of cross-validation (SFCV) = 97.53%, while the validation set had a R2=0.624 with a low standard error of prediction (SEP= 205.801 %)

Intellectual arrogance and intellectual humility: correlational evidence for an evolutionary-embodied-epistemological account

We outline an evolutionary-embodied-epistemological (EEE) account of intellectual arrogance (IA), proposing that people psychologically experience their important beliefs as valued possessions – mental materialism – that they must fight to keep – ideological territoriality – thereby disposing them toward IA. Nonetheless, IA should still vary, being higher among people taking a hostile and domineering epistemic stance (rejecting reality, resisting evidence) than among those taking an open and deferential one (embracing reality, respecting evidence). Such variations can be predicted from people’s standing on the communion-agency circumplex at multiple levels of analysis (i.e. from their social inclusion and status; dispositional warmth and competence; and behavioral amiability and assertiveness). Using pre-validated indices of mental materialism and ideological territoriality, and an argument evaluation task permitting the quantification of rational objectivity and egotistical bias, we obtained consistent correlational evidence that, as hypothesized, IA is the highest when agency is high and communion low, validating the EEE account

A Comparative Study of SARS COVID-19 Using X-ray and CT Scan Images Using Deep Learning Techniques

Coronavirus disease (Covid-19) first came to light in 2019. It has spread across the world, which has led to a very strong ephemeral. As a result of its infection rate and severity, it has emerged as one of the significant ultimatums of the present generation. Since then, many academics and clinicians have worked on artificial intelligence-based models to predict Covid-19. It is easier to assess and cure the disease if it is detected and expected in an early stage. Moreover, using deep learning model techniques can decrease the time spent on traditional practices. As a result, our study stresses the use of deep learning patterns for interpreting medical images in Covid-19 patients. In addition, a comparison of numerous strategies for developing a deep learning model has been carried out. The goal is to create a deep learning architecture and compare and analyze performance metrics like accuracy, recall, precision, and F-score for deep learning methods used to detect SARS Covid-19 in an automated manner

Aza-heterocyclic Receptors for Direct Electron Transfer Hemoglobin Biosensor

Direct Electron Transfer biosensors, facilitating direct communication between the biomolecule of interest and electrode surface, are preferable compared to enzymatic and mediator based sensors. Although hemoglobin (Hb) contains four redox active iron centres, direct detection is not possible due to inaccessibility of iron centres and formation of dimers, blocking electron transfer. Through the coordination of iron with aza-heterocyclic receptors-pyridine and imidazole-we report a cost effective, highly sensitive and simple electrochemical Hb sensor using cyclic voltammetry and chronoamperometry. The receptor can be either in the form of liquid micro-droplet mixed with blood or dry chemistry embedded in paper membrane on top of screen printed carbon electrodes. We demonstrate excellent linearity and robustness against interference using clinical samples. A truly point of care technology is demonstrated by integrating disposable test strips with handheld reader, enabling finger prick to result in less than a minute