Growth performances of three microalgal species in filtered brackishwater with different inorganic media

The growth of three microalgae species, viz., Nannochloropsis oculata, Tetraselmis chui and Chaetoceros muelleri which are commonly used in aquaculture, was investigated using three different inorganic nutrient media: (i) Modified Guillard's f/2 medium (ii) Rix Mix medium and (iii) BFRI medium. Each microalgae species was cultured for 24 days in small- scale with initial inoculation density of 17xl04 cell /ml in the three media with triplicates. N. oculata cultured in modified Guillard's f/2 medium showed superior growth with a mean peak density of 221 ±4.24 x 104 cell/ ml, to Rix Mix medium (141 ± 10.54xl04 cell/ml) and BFRI medium (47±4.94 x 104 cell/ml) on the 16th day of culture at stationary phase. Considering the increase in cell density for 20 days of culture in Rix Mix medium, C. muelleriwas significantly (P<0.05) highest than in other two media. N. oculata cultured in BFRI medium resulted in the poorest growth with a mean peak increase in density of 84±9.19 x 104 cell/ml in 12 days of culture. However, with an increase in cell density, growth of T. chui (182 ± 6.26 x 104 cell/ml) was significantly (P<0.05) higher in BFRI medium than in modified Guillard's f/2 medium. The results of the present study suggest that N. oculata and C. muelleri can be grown very well in both the modified Guillard's f/2 medium and Rix Mix medium. Better growth of T. chui can be obtained while culturing either in BFRI and Rix Mix medium. These three nutrient media used in the present study may be useful for microalgae species culture for establishing green-water culture for suitable target zooplankton, and fish and crustacean larvae in marine and brackishwater hatcheries

Study on the effect of feeding different levels of energy in compound Pellet on performance of growing black Bengal Goat

Three different complete compound pellets containing different levels of energy, viz. SE (standard energy, ME content 10.28MJ/kg DM as per NRC, 1981), LE (low energy, ME content 9.25MJ/kg DM) 10% less ME and HE (high energy, ME content 11.30MJ/kg DM) 10% high ME than SE respectively, were prepared and fed to three groups of growing Black Bengal goats for evaluating feeding value. Completely randomized design was followed in the experiment. The effect of different levels of energy containing pellet on performance of goat was varied. Both dietary group SE and HE showed higher (p<0.01) weight gain, total CPI, total MEI, and better (p<0.05) FCR and PCR than dietary group LE and only dietary group HE showed higher (p<0.05) total DMI, MEI 100kg-1 LW d-1 and MEI kg-1W0.75 d-1 than LE and DMI kg-1W0.75 d-1 than SE and LE. Higher (p<0.01) digestibility of DM, OM, ADF, NDF and (p<0.05) CP and CF was observed in SE and HE and digestibility of NFE in HE was higher (p<0.01) than SE and LE. On the other hand, higher (p<0.05) digestibility of EE was observed in SE and LE. Digestible crude protein, TDN and D value were higher (p<0.01) in SE and HE. Digestible EE was highest (p<0.01) in LE and lowest in HE but digestible NFE was highest (p<0.01) in HE and lowest in LE. Higher (p0.05) nitrogen retention was observed in SE and HE. Meat yield, selling price of meat, and total price was highest in HE and both SE and HE showed higher (p<0.01) value of the parameters than LE. Total rearing cost was highest (p<0.01) in HE and lowest in LE. Higher (p<0.05) net profit was obtained from SE and HE than LE. There was a positive correlation between increase of energy in dietary pellet and performance. It can be concluded that high energy containing pellet may be used for commercial Black Bengal goat production in stall feeding

Integrated use of Biochar: A tool for improving soil and wheat quality of degraded soil under wheat-maiza cropping pattern

Wheat quality, nutrient uptake and nutrient use efficiency are significantly influenced by nutrient sources and application rate. To investigate the integrative effect of biochar, farmyard manure (FYM) and nitrogen (organic and inorganic soil amendments) in a wheat-maize cropping system, a two year study was designed to assess the interactive outcome of biochar, FYM and nitrogenous fertilizer on wheat nitrogen (N) parameters and associated soil quality parameters. Three levels of biochar (0, 25 and 50 t ha-1), two levels of FYM (5 and 10 t ha-1) and two levels of nitrogen fertilizer (60 and 120 kg ha-1) were used in the study. Biochar application displayed a significantly increased in wheat leaf, stem, straw and grain N content; grain and total N-uptake and grain protein content by 24, 20, 24, 56, 50, 17 and 20% respectively. Similarly, biochar application significantly increased soil total N (TN) and soil mineral N (SMN) by 63 and 40% respectively in second year. FYM application increased grain, leaf and straw N content by 20, 19.5 and 18% respectively, and increased total N-uptake and grain protein content by 49 and 19% respectively. FYM increased soil TN and SMN by 63 and 32% in both the years of the experiment. Mineral N application increased soil TN by over a half and SMN by a third, and grain protein content increased 16%. In contrast, nitrogen use efficiency (NUE) decreased for all amendments relative to the control. However, biochar treated plots improved NUE by 38% compared to plots without biochar. In conclusion, this field experiment has illustrated the potential of biochar to bring about short-term benefits in wheat and soil quality parameters in wheat-maize cropping systems. However, the long-term benefits remain to be quantified

Psychological impact of COVID-19 pandemic on health care workers of tertiary care hospit

Healthcare workers (HCWs) are at increased risk of mental health issues when faced with the challenges associated with pandemics. This study was conducted to assess the psychological impact of pandemic o n HCWs working in tertiary care hospitals of Khyber-Pakhtunkhwa province of Pakistan. This cross-sectional study was conducted between April & June 2020. By convenience sampling an electronic form of Goldberg General Health Questionnaire was distributed among HCWs of the private sector and public tertiary care hospitals. Data were analyzed using SPSS version 22. Inferential analysis was done. The significant level was considered at p=<0.05. Total of 186 HCWs among which 105 (56.5%) males and 81 (43.5%) females participated in the survey, a mean age of 37.6±9.28 years. The highest prevalence was found for social dysfunction 184 (97.8%) followed by somatization, 169 (92.8%). Significance of difference was found between age group and anxiety (p=0.018), specialty of HCWs with somatization and social dysfunction (p=0.041 and 0.037 respectively). Pandemic poses a significant risk for the mental health of HCWs. During pandemics at its peak, proper mental health support program, personal and family protection assurance is highly recommended for provision of quality care by HCWs

EEG-based multi-modal emotion recognition using bag of deep features: An optimal feature selection approach

Much attention has been paid to the recognition of human emotions with the help of electroencephalogram (EEG) signals based on machine learning technology. Recognizing emotions is a challenging task due to the non-linear property of the EEG signal. This paper presents an advanced signal processing method using the deep neural network (DNN) for emotion recognition based on EEG signals. The spectral and temporal components of the raw EEG signal are first retained in the 2D Spectrogram before the extraction of features. The pre-trained AlexNet model is used to extract the raw features from the 2D Spectrogram for each channel. To reduce the feature dimensionality, spatial, and temporal based, bag of deep features (BoDF) model is proposed. A series of vocabularies consisting of 10 cluster centers of each class is calculated using the k-means cluster algorithm. Lastly, the emotion of each subject is represented using the histogram of the vocabulary set collected from the raw-feature of a single channel. Features extracted from the proposed BoDF model have considerably smaller dimensions. The proposed model achieves better classification accuracy compared to the recently reported work when validated on SJTU SEED and DEAP data sets. For optimal classification performance, we use a support vector machine (SVM) and k-nearest neighbor (k-NN) to classify the extracted features for the different emotional states of the two data sets. The BoDF model achieves 93.8% accuracy in the SEED data set and 77.4% accuracy in the DEAP data set, which is more accurate compared to other state-of-the-art methods of human emotion recognition. - 2019 by the authors. Licensee MDPI, Basel, Switzerland.Funding: This research was funded by Higher Education Commission (HEC): Tdf/67/2017.Scopu